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    Characterizing social networks

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    People tend to connect with others who are like them. Alumni from the same alma mater are more likely to collaborate over a research project together, or individuals with the same political beliefs are more likely to join the same political parties, attend rallies, and engage in online discussions. This sociology concept, called homophily, has been observed in many network science studies. But if like-minded individuals cluster in online and offline spaces to reinforce each other’s ideas and form synergies, what does that mean for society?

    Researchers at MIT wanted to investigate homophily further to understand how groups of three or more interact in complex societal settings. Prior research on understanding homophily has studied relationships between pairs of people. For example, when two members of Congress co-sponsor a bill, they are likely to be from the same political party.

    However, less is known about whether group interactions between three or more people are likely to occur between similar individuals. If three members of Congress co-sponsor a bill together, are all three likely to be members of the same party, or would we expect more bipartisanship? When the researchers tried to extend traditional methods to measure homophily in these larger group interactions, they found the results can be misleading.

    “We found that homophily observed in pairs, or one-to-one interactions, can make it seem like there’s more homophily in larger groups than there really is,” says Arnab Sarker, graduate student in the Institute for Data, Systems and Society (IDSS) and lead author of the study published in Proceedings of the National Academy of Sciences. “The previous measure didn’t account for the way in which two people already know each other in friendship settings,” he adds.

    To address this issue, Sarker, along with co-authors Natalie Northrup ’22 and Ali Jadbabaie, the JR East Professor of Engineering, head of the Department of Civil and Environmental Engineering, and core faculty member of IDSS, developed a new way of measuring homophily. Borrowing tools from algebraic topology, a subfield in mathematics typically applied in physics, they developed a new measure to understand whether homophily occurred in group interactions.

    The new measure, called simplicial homophily, separates the homophily seen in one-on-one interactions from those in larger group interactions and is based on the mathematical concept of a simplicial complex. The researchers tested this new measure with real-world data from 16 different datasets and found that simplicial homophily provides more accurate insights into how similar things interact in larger groups. Interestingly, the new measure can better identify instances where there is a lack of similarity in larger group interactions, thus rectifying a weakness observed in the previous measure.

    One such example of this instance was demonstrated in the dataset from the global hotel booking website, Trivago. They found that when travelers are looking at two hotels in one session, they often pick hotels that are close to one another geographically. But when they look at more than two hotels in one session, they are more likely to be searching for hotels that are farther apart from one another (for example, if they are taking a vacation with multiple stops). The new method showed “anti-homophily” — instead of similar hotels being chosen together, different hotels were chosen together.

    “Our measure controls for pairwise connections and is suggesting that there’s more diversity in the hotels that people are looking for as group size increases, which is an interesting economic result,” says Sarker.

    Additionally, they discovered that simplicial homophily can help identify when certain characteristics are important for predicting if groups will interact in the future. They found that when there’s a lot of similarity or a lot of difference between individuals who already interact in groups, then knowing individual characteristics can help predict their connection to each other in the future.

    Northrup was an undergraduate researcher on the project and worked with Sarker and Jadbabaie over three semesters before she graduated. The project gave her an opportunity to take some of the concepts she learned in the classroom and apply them.

    “Working on this project, I really dove into building out the higher-order network model, and understanding the network, the math, and being able to implement it at a large scale,” says Northrup, who was in the civil and environmental engineering systems track with a double major in economics.

    The new measure opens up opportunities to study complex group interactions in a broad range of network applications, from ecology to traffic and socioeconomics. One of the areas Sarker has interest in exploring is the group dynamics of people finding jobs through social networks. “Does higher-order homophily affect how people get information about jobs?” he asks.    

    Northrup adds that it could also be used to evaluate interventions or specific policies to connect people with job opportunities outside of their network. “You can even use it as a measurement to evaluate how effective that might be.”

    The research was supported through funding from a Vannevar Bush Fellowship from the Office of the U.S. Secretary of Defense and from the U.S. Army Research Office Multidisciplinary University Research Initiative. More

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      Advancing social studies at MIT Sloan

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      Around 2010, Facebook was a relatively small company with about 2,000 employees. So, when a PhD student named Dean Eckles showed up to serve an intership at the firm, he landed in a position with some real duties.

      Eckles essentially became the primary data scientist for the product manager who was overseeing the platform’s news feeds. That manager would pepper Eckles with questions. How exactly do people influence each other online? If Facebook tweaked its content-ranking algorithms, what would happen? What occurs when you show people more photos?

      As a doctoral candidate already studying social influence, Eckles was well-equipped to think about such questions, and being at Facebook gave him a lot of data to study them. 

      “If you show people more photos, they post more photos themselves,” Eckles says. “In turn, that affects the experience of all their friends. Plus they’re getting more likes and more comments. It affects everybody’s experience. But can you account for all of these compounding effects across the network?”

      Eckles, now an associate professor in the MIT Sloan School of Management and an affiliate faculty member of the Institute for Data, Systems, and Society, has made a career out of thinking carefully about that last question. Studying social networks allows Eckles to tackle significant questions involving, for example, the economic and political effects of social networks, the spread of misinformation, vaccine uptake during the Covid-19 crisis, and other aspects of the formation and shape of social networks. For instance, one study he co-authored this summer shows that people who either move between U.S. states, change high schools, or attend college out of state, wind up with more robust social networks, which are strongly associated with greater economic success.

      Eckles maintains another research channel focused on what scholars call “causal inference,” the methods and techniques that allow researchers to identify cause-and-effect connections in the world.

      “Learning about cause-and-effect relationships is core to so much science,” Eckles says. “In behavioral, social, economic, or biomedical science, it’s going to be hard. When you start thinking about humans, causality gets difficult. People do things strategically, and they’re electing into situations based on their own goals, so that complicates a lot of cause-and-effect relationships.”

      Eckles has now published dozens of papers in each of his different areas of work; for his research and teaching, Eckles received tenure from MIT last year.

      Five degrees and a job

      Eckles grew up in California, mostly near the Lake Tahoe area. He attended Stanford University as an undergraduate, arriving on campus in fall 2002 — and didn’t really leave for about a decade. Eckles has five degrees from Stanford. As an undergrad, he received a BA in philosophy and a BS in symbolic systems, an interdisciplinary major combining computer science, philosophy, psychology, and more. Eckles was set to attend Oxford University for graduate work in philosophy but changed his mind and stayed at Stanford for an MS in symbolic systems too. 

      “[Oxford] might have been a great experience, but I decided to focus more on the tech side of things,” he says.

      After receiving his first master’s degree, Eckles did take a year off from school and worked for Nokia, although the firm’s offices were adjacent to the Stanford campus and Eckles would sometimes stop and talk to faculty during the workday. Soon he was enrolled at Stanford again, this time earning his PhD in communication, in 2012, while receiving an MA in statistics the year before. His doctoral dissertation wound up being about peer influence in networks. PhD in hand, Eckles promptly headed back to Facebook, this time for three years as a full-time researcher.

       “They were really supportive of the work I was doing,” Eckles says.

      Still, Eckles remained interested in moving into academia, and joined the MIT faculty in 2017 with a position in MIT Sloan’s Marketing Group. The group consists of a set of scholars with far-ranging interests, from cognitive science to advertising to social network dynamics.

      “Our group reflects something deeper about the Sloan school and about MIT as well, an openness to doing things differently and not having to fit into narrowly defined tracks,” Eckles says.

      For that matter, MIT has many faculty in different domains who work on causal inference, and whose work Eckles quickly cites — including economists Victor Chernozhukov and Alberto Abadie, and Joshua Angrist, whose book “Mostly Harmless Econometrics” Eckles name-checks as an influence.

      “I’ve been fortunate in my career that causal inference turned out to be a hot area,” Eckles says. “But I think it’s hot for good reasons. People started to realize that, yes, causal inference is really important. There are economists, computer scientists, statisticians, and epidemiologists who are going to the same conferences and citing each other’s papers. There’s a lot happening.”

      How do networks form?

      These days, Eckles is interested in expanding the questions he works on. In the past, he has often studied existing social networks and looked at their effects. For instance: One study Eckles co-authored, examining the 2012 U.S. elections, found that get-out-the-vote messages work very well, especially when relayed via friends.

      That kind of study takes the existence of the network as a given, though. Another kind of research question is, as Eckles puts it, “How do social networks form and evolve? And what are the consequences of these network structures?” His recent study about social networks expanding as people move around and change schools is one example of research that digs into the core life experiences underlying social networks.

      “I’m excited about doing more on how these networks arise and what factors, including everything from personality to public transit, affect their formation,” Eckles says.

      Understanding more about how social networks form gets at key questions about social life and civic structure. Suppose research shows how some people develop and maintain beneficial connections in life; it’s possible that those insights could be applied to programs helping people in more disadvantaged situations realize some of the same opportunities.

      “We want to act on things,” Eckles says. “Sometimes people say, ‘We care about prediction.’ I would say, ‘We care about prediction under intervention.’ We want to predict what’s going to happen if we try different things.”

      Ultimately, Eckles reflects, “Trying to reason about the origins and maintenance of social networks, and the effects of networks, is interesting substantively and methodologically. Networks are super-high-dimensional objects, even just a single person’s network and all its connections. You have to summarize it, so for instance we talk about weak ties or strong ties, but do we have the correct description? There are fascinating questions that require development, and I’m eager to keep working on them.”   More

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      Frequent encounters build familiarity

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      Do better spatial networks make for better neighbors? There is evidence that they do, according to Paige Bollen, a sixth-year political science graduate student at MIT. The networks Bollen works with are not virtual but physical, part of the built environment in which we are all embedded. Her research on urban spaces suggests that the routes bringing people together or keeping them apart factor significantly in whether individuals see each other as friend or foe.

      “We all live in networks of streets, and come across different types of people,” says Bollen. “Just passing by others provides information that informs our political and social views of the world.” In her doctoral research, Bollen is revealing how physical context matters in determining whether such ordinary encounters engender suspicion or even hostility, while others can lead to cooperation and tolerance.

      Through her in-depth studies mapping the movement of people in urban communities in Ghana and South Africa, Bollen is demonstrating that even in diverse communities, “when people repeatedly come into contact, even if that contact is casual, they can build understanding that can lead to cooperation and positive outcomes,” she says. “My argument is that frequent, casual contact, facilitated by street networks, can make people feel more comfortable with those unlike themselves,” she says.

      Mapping urban networks

      Bollen’s case for the benefits of casual contact emerged from her pursuit of several related questions: Why do people in urban areas who regard other ethnic groups with prejudice and economic envy nevertheless manage to collaborate for a collective good? How do you reduce fears that arise from differences? How do the configuration of space and the built environment influence contact patterns among people?

      While other social science research suggests that there are weak ties in ethnically mixed urban communities, with casual contact exacerbating hostility, Bollen noted that there were plenty of examples of “cooperation across ethnic divisions in ethnically mixed communities.” She absorbed the work of psychologist Stanley Milgram, whose 1972 research showed that strangers seen frequently in certain places become familiar — less anonymous or threatening. So she set out to understand precisely how “the built environment of a neighborhood interacts with its demography to create distinct patterns of contact between social groups.”

      With the support of MIT Global Diversity Lab and MIT GOV/LAB, Bollen set out to develop measures of intergroup contact in cities in Ghana and South Africa. She uses street network data to predict contact patterns based on features of the built environment and then combines these measures with mobility data on peoples’ actual movement.

      “I created a huge dataset for every intersection in these cities, to determine the central nodes where many people are passing through,” she says. She combined these datasets with census data to determine which social groups were most likely to use specific intersections based on their position in a particular street network. She mapped these measures of casual contact to outcomes, such as inter-ethnic cooperation in Ghana and voting behavior in South Africa.

      “My analysis [in Ghana] showed that in areas that are more ethnically heterogeneous and where there are more people passing through intersections, we find more interconnections among people and more cooperation within communities in community development efforts,” she says.

      In a related survey experiment conducted on Facebook with 1,200 subjects, Bollen asked Accra residents if they would help an unknown non-co-ethnic in need with a financial gift. She found that the likelihood of offering such help was strongly linked to the frequency of interactions. “Helping behavior occurred when the subjects believed they would see this person again, even when they did not know the person in need well,” says Bollen. “They figured if they helped, they could count on this person’s reciprocity in the future.”

      For Bollen, this was “a powerful gut check” for her hypothesis that “frequency builds familiarity, because frequency provides information and drives expectations, which means it can reduce uncertainty and fear of the other.”

      In research underway in South Africa, a nation increasingly dealing with anti-immigrant violence, Bollen is investigating whether frequency of contact reduces prejudice against foreigners. Using her detailed street maps, 1.1 billion unique geolocated cellphone pings, and election data, she finds that frequent contact opportunities with immigrants are associated with lower support for anti-immigrant party voting.    Passion for places and spaces

      Bollen never anticipated becoming a political scientist. The daughter of two academics, she was “bent on becoming a data scientist.” But she was also “always interested in why people behave in certain ways and how this influences macro trends.”

      As an undergraduate at Tufts University, she became interested in international affairs. But it was her 2013 fieldwork studying women-only carriages in Delhi, India’s metro system, that proved formative. “I interviewed women for a month, talking to them about how these cars enabled them to participate in public life,” she recalls. Another project involving informal transportation routes in Cape Town, South Africa, immersed her more deeply in the questions of people’s experience of public space. “I left college thinking about mobility and public space, and I discovered how much I love geographic information systems,” she says.

      A gig with the Commonwealth of Massachusetts to improve the 911 emergency service — updating and cleaning geolocations of addresses using Google Street View — further piqued her interest. “The job was tedious, but I realized you can really understand a place, and how people move around, from these images.” Bollen began thinking about a career in urban planning.

      Then a two-year stint as a researcher at MIT GOV/LAB brought Bollen firmly into the political science fold. Working with Lily Tsai, the Ford Professor of Political Science, on civil society partnerships in the developing world, Bollen realized that “political science wasn’t what I thought it was,” she says. “You could bring psychology, economics, and sociology into thinking about politics.” Her decision to join the doctoral program was simple: “I knew and loved the people I was with at MIT.”

      Bollen has not regretted that decision. “All the things I’ve been interested in are finally coming together in my dissertation,” she says. Due to the pandemic, questions involving space, mobility, and contact became sharper to her. “I shifted my research emphasis from asking people about inter-ethnic differences and inequality through surveys, to using contact and context information to measure these variables.”

      She sees a number of applications for her work, including working with civil society organizations in communities touched by ethnic or other frictions “to rethink what we know about contact, challenging some of the classic things we think we know.”

      As she moves into the final phases of her dissertation, which she hopes to publish as a book, Bollen also relishes teaching comparative politics to undergraduates. “There’s something so fun engaging with them, and making their arguments stronger,” she says. With the long process of earning a PhD, this helps her “enjoy what she is doing every single day.” More

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      Systems scientists find clues to why false news snowballs on social media

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      The spread of misinformation on social media is a pressing societal problem that tech companies and policymakers continue to grapple with, yet those who study this issue still don’t have a deep understanding of why and how false news spreads.

      To shed some light on this murky topic, researchers at MIT developed a theoretical model of a Twitter-like social network to study how news is shared and explore situations where a non-credible news item will spread more widely than the truth. Agents in the model are driven by a desire to persuade others to take on their point of view: The key assumption in the model is that people bother to share something with their followers if they think it is persuasive and likely to move others closer to their mindset. Otherwise they won’t share.

      The researchers found that in such a setting, when a network is highly connected or the views of its members are sharply polarized, news that is likely to be false will spread more widely and travel deeper into the network than news with higher credibility.

      This theoretical work could inform empirical studies of the relationship between news credibility and the size of its spread, which might help social media companies adapt networks to limit the spread of false information.

      “We show that, even if people are rational in how they decide to share the news, this could still lead to the amplification of information with low credibility. With this persuasion motive, no matter how extreme my beliefs are — given that the more extreme they are the more I gain by moving others’ opinions — there is always someone who would amplify [the information],” says senior author Ali Jadbabaie, professor and head of the Department of Civil and Environmental Engineering and a core faculty member of the Institute for Data, Systems, and Society (IDSS) and a principal investigator in the Laboratory for Information and Decision Systems (LIDS).

      Joining Jadbabaie on the paper are first author Chin-Chia Hsu, a graduate student in the Social and Engineering Systems program in IDSS, and Amir Ajorlou, a LIDS research scientist. The research will be presented this week at the IEEE Conference on Decision and Control.

      Pondering persuasion

      This research draws on a 2018 study by Sinan Aral, the David Austin Professor of Management at the MIT Sloan School of Management; Deb Roy, an associate professor of media arts and sciences at the Media Lab; and former postdoc Soroush Vosoughi (now an assistant professor of computer science at Dartmouth University). Their empirical study of data from Twitter found that false news spreads wider, faster, and deeper than real news.

      Jadbabaie and his collaborators wanted to drill down on why this occurs.

      They hypothesized that persuasion might be a strong motive for sharing news — perhaps agents in the network want to persuade others to take on their point of view — and decided to build a theoretical model that would let them explore this possibility.

      In their model, agents have some prior belief about a policy, and their goal is to persuade followers to move their beliefs closer to the agent’s side of the spectrum.

      A news item is initially released to a small, random subgroup of agents, which must decide whether to share this news with their followers. An agent weighs the newsworthiness of the item and its credibility, and updates its belief based on how surprising or convincing the news is. 

      “They will make a cost-benefit analysis to see if, on average, this piece of news will move people closer to what they think or move them away. And we include a nominal cost for sharing. For instance, taking some action, if you are scrolling on social media, you have to stop to do that. Think of that as a cost. Or a reputation cost might come if I share something that is embarrassing. Everyone has this cost, so the more extreme and the more interesting the news is, the more you want to share it,” Jadbabaie says.

      If the news affirms the agent’s perspective and has persuasive power that outweighs the nominal cost, the agent will always share the news. But if an agent thinks the news item is something others may have already seen, the agent is disincentivized to share it.

      Since an agent’s willingness to share news is a product of its perspective and how persuasive the news is, the more extreme an agent’s perspective or the more surprising the news, the more likely the agent will share it.

      The researchers used this model to study how information spreads during a news cascade, which is an unbroken sharing chain that rapidly permeates the network.

      Connectivity and polarization

      The team found that when a network has high connectivity and the news is surprising, the credibility threshold for starting a news cascade is lower. High connectivity means that there are multiple connections between many users in the network.

      Likewise, when the network is largely polarized, there are plenty of agents with extreme views who want to share the news item, starting a news cascade. In both these instances, news with low credibility creates the largest cascades.

      “For any piece of news, there is a natural network speed limit, a range of connectivity, that facilitates good transmission of information where the size of the cascade is maximized by true news. But if you exceed that speed limit, you will get into situations where inaccurate news or news with low credibility has a larger cascade size,” Jadbabaie says.

      If the views of users in the network become more diverse, it is less likely that a poorly credible piece of news will spread more widely than the truth.

      Jadbabaie and his colleagues designed the agents in the network to behave rationally, so the model would better capture actions real humans might take if they want to persuade others.

      “Someone might say that is not why people share, and that is valid. Why people do certain things is a subject of intense debate in cognitive science, social psychology, neuroscience, economics, and political science,” he says. “Depending on your assumptions, you end up getting different results. But I feel like this assumption of persuasion being the motive is a natural assumption.”

      Their model also shows how costs can be manipulated to reduce the spread of false information. Agents make a cost-benefit analysis and won’t share news if the cost to do so outweighs the benefit of sharing.

      “We don’t make any policy prescriptions, but one thing this work suggests is that, perhaps, having some cost associated with sharing news is not a bad idea. The reason you get lots of these cascades is because the cost of sharing the news is actually very low,” he says.

      This work was supported by an Army Research Office Multidisciplinary University Research Initiative grant and a Vannevar Bush Fellowship from the Office of the Secretary of Defense. More

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      Lincoln Laboratory convenes top network scientists for Graph Exploitation Symposium

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      As the Covid-19 pandemic has shown, we live in a richly connected world, facilitating not only the efficient spread of a virus but also of information and influence. What can we learn by analyzing these connections? This is a core question of network science, a field of research that models interactions across physical, biological, social, and information systems to solve problems.

      The 2021 Graph Exploitation Symposium (GraphEx), hosted by MIT Lincoln Laboratory, brought together top network science researchers to share the latest advances and applications in the field.

      “We explore and identify how exploitation of graph data can offer key technology enablers to solve the most pressing problems our nation faces today,” says Edward Kao, a symposium organizer and technical staff in Lincoln Laboratory’s AI Software Architectures and Algorithms Group.

      The themes of the virtual event revolved around some of the year’s most relevant issues, such as analyzing disinformation on social media, modeling the pandemic’s spread, and using graph-based machine learning models to speed drug design.

      “The special sessions on influence operations and Covid-19 at GraphEx reflect the relevance of network and graph-based analysis for understanding the phenomenology of these complicated and impactful aspects of modern-day life, and also may suggest paths forward as we learn more and more about graph manipulation,” says William Streilein, who co-chaired the event with Rajmonda Caceres, both of Lincoln Laboratory.

      Social networks

      Several presentations at the symposium focused on the role of network science in analyzing influence operations (IO), or organized attempts by state and/or non-state actors to spread disinformation narratives.  

      Lincoln Laboratory researchers have been developing tools to classify and quantify the influence of social media accounts that are likely IO accounts, such as those willfully spreading false Covid-19 treatments to vulnerable populations.

      “A cluster of IO accounts acts as an echo chamber to amplify the narrative. The vulnerable population is then engaging in these narratives,” says Erika Mackin, a researcher developing the tool, called RIO or Reconnaissance of Influence Operations.

      To classify IO accounts, Mackin and her team trained an algorithm to detect probable IO accounts in Twitter networks based on a specific hashtag or narrative. One example they studied was #MacronLeaks, a disinformation campaign targeting Emmanuel Macron during the 2017 French presidential election. The algorithm is trained to label accounts within this network as being IO on the basis of several factors, such as the number of interactions with foreign news accounts, the number of links tweeted, or number of languages used. Their model then uses a statistical approach to score an account’s level of influence in spreading the narrative within that network.

      The team has found that their classifier outperforms existing detectors of IO accounts, because it can identify both bot accounts and human-operated ones. They’ve also discovered that IO accounts that pushed the 2017 French election disinformation narrative largely overlap with accounts influentially spreading Covid-19 pandemic disinformation today. “This suggests that these accounts will continue to transition to disinformation narratives,” Mackin says.

      Pandemic modeling

      Throughout the Covid-19 pandemic, leaders have been looking to epidemiological models, which predict how disease will spread, to make sound decisions. Alessandro Vespignani, director of the Network Science Institute at Northeastern University, has been leading Covid-19 modeling efforts in the United States, and shared a keynote on this work at the symposium.

      Besides taking into account the biological facts of the disease, such as its incubation period, Vespignani’s model is especially powerful in its inclusion of community behavior. To run realistic simulations of disease spread, he develops “synthetic populations” that are built by using publicly available, highly detailed datasets about U.S. households. “We create a population that is not real, but is statistically real, and generate a map of the interactions of those individuals,” he says. This information feeds back into the model to predict the spread of the disease. 

      Today, Vespignani is considering how to integrate genomic analysis of the virus into this kind of population modeling in order to understand how variants are spreading. “It’s still a work in progress that is extremely interesting,” he says, adding that this approach has been useful in modeling the dispersal of the Delta variant of SARS-CoV-2. 

      As researchers model the virus’ spread, Lucas Laird at Lincoln Laboratory is considering how network science can be used to design effective control strategies. He and his team are developing a model for customizing strategies for different geographic regions. The effort was spurred by the differences in Covid-19 spread across U.S. communities, and what the researchers found to be a gap in intervention modeling to address those differences.

      As examples, they applied their planning algorithm to three counties in Florida, Massachusetts, and California. Taking into account the characteristics of a specific geographic center, such as the number of susceptible individuals and number of infections there, their planner institutes different strategies in those communities throughout the outbreak duration.

      “Our approach eradicates disease in 100 days, but it also is able to do it with much more targeted interventions than any of the global interventions. In other words, you don’t have to shut down a full country.” Laird adds that their planner offers a “sandbox environment” for exploring intervention strategies in the future.

      Machine learning with graphs

      Graph-based machine learning is receiving increasing attention for its potential to “learn” the complex relationships between graphical data, and thus extract new insights or predictions about these relationships. This interest has given rise to a new class of algorithms called graph neural networks. Today, graph neural networks are being applied in areas such as drug discovery and material design, with promising results.

      “We can now apply deep learning much more broadly, not only to medical images and biological sequences. This creates new opportunities in data-rich biology and medicine,” says Marinka Zitnik, an assistant professor at Harvard University who presented her research at GraphEx.

      Zitnik’s research focuses on the rich networks of interactions between proteins, drugs, disease, and patients, at the scale of billions of interactions. One application of this research is discovering drugs to treat diseases with no or few approved drug treatments, such as for Covid-19. In April, Zitnik’s team published a paper on their research that used graph neural networks to rank 6,340 drugs for their expected efficacy against SARS-CoV-2, identifying four that could be repurposed to treat Covid-19.

      At Lincoln Laboratory, researchers are similarly applying graph neural networks to the challenge of designing advanced materials, such as those that can withstand extreme radiation or capture carbon dioxide. Like the process of designing drugs, the trial-and-error approach to materials design is time-consuming and costly. The laboratory’s team is developing graph neural networks that can learn relationships between a material’s crystalline structure and its properties. This network can then be used to predict a variety of properties from any new crystal structure, greatly speeding up the process of screening materials with desired properties for specific applications.

      “Graph representation learning has emerged as a rich and thriving research area for incorporating inductive bias and structured priors during the machine learning process, with broad applications such as drug design, accelerated scientific discovery, and personalized recommendation systems,” Caceres says. 

      A vibrant community

      Lincoln Laboratory has hosted the GraphEx Symposium annually since 2010, with the exception of last year’s cancellation due to Covid-19. “One key takeaway is that despite the postponement from last year and the need to be virtual, the GraphEx community is as vibrant and active as it’s ever been,” Streilein says. “Network-based analysis continues to expand its reach and is applied to ever-more important areas of science, society, and defense with increasing impact.”

      In addition to those from Lincoln Laboratory, technical committee members and co-chairs of the GraphEx Symposium included researchers from Harvard University, Arizona State University, Stanford University, Smith College, Duke University, the U.S. Department of Defense, and Sandia National Laboratories. More