1.3k citations · 3.7k across the 55 of their papers we have counts for
35 papers · 1 filter
Hypergraph patterns and collaboration structure
Jonas L. Juul, Austin R. Benson, Jon Kleinberg
Humans collaborate in different contexts such as in creative or scientific projects, in workplaces and in sports. Depending on the project and external circumstances, a newly forme…
fauci-email: a json digest of Anthony Fauci's released emails
Austin R. Benson, Nate Veldt, David F. Gleich
A collection of over 3000 pages of emails sent by Anthony Fauci and his staff were released in an effort to understand the United States government response to the COVID-19 pandemi…
Capturing Tie Strength with Algebraic Topology
Arnab Sarker, Jean-Baptiste Seby, Austin R. Benson +1
The association between tie strength and social structure is a fundamental topic in the social sciences. We study this association by analyzing tie strength in higher-order network…
Edge Proposal Sets for Link Prediction
Abhay Singh, Qian Huang, Sijia Linda Huang +4
Graphs are a common model for complex relational data such as social networks and protein interactions, and such data can evolve over time (e.g., new friendships) and be noisy (e.g…
Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data
Austin R. Benson, David F. Gleich, Desmond J. Higham
Higher-order network analysis uses the ideas of hypergraphs, simplicial complexes, multilinear and tensor algebra, and more, to study complex systems. These are by now well establi…
Combinatorial Characterizations and Impossibilities for Higher-order Homophily
Nate Veldt, Austin R. Benson, Jon Kleinberg
Homophily is the seemingly ubiquitous tendency for people to connect and interact with other individuals who are similar to them. This is a well-documented principle and is fundame…
Random Graphs with Prescribed -Core Sequences: A New Null Model for Network Analysis
Katherine Van Koevering, Austin R. Benson, Jon Kleinberg
In the analysis of large-scale network data, a fundamental operation is the comparison of observed phenomena to the predictions provided by null models: when we find an interesting…
Generative hypergraph clustering: from blockmodels to modularity
Philip S. Chodrow, Nate Veldt, Austin R. Benson
Hypergraphs are a natural modeling paradigm for a wide range of complex relational systems. A standard analysis task is to identify clusters of closely related or densely interconn…
A simple bipartite graph projection model for clustering in networks
Austin R. Benson, Paul Liu, Hao Yin
Graph datasets are frequently constructed by a projection of a bipartite graph, where two nodes are connected in the projection if they share a common neighbor in the bipartite gra…
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform
Derek Lim, Austin R. Benson
Many platforms collect crowdsourced information primarily from volunteers. As this type of knowledge curation has become widespread, contribution formats vary substantially and are…
Hypergraph Clustering for Finding Diverse and Experienced Groups
Ilya Amburg, Nate Veldt, Austin R. Benson
When forming a team or group of individuals, we often seek a balance of expertise in a particular task while at the same time maintaining diversity of skills within each group. Her…
Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text
Katherine Van Koevering, Austin R. Benson, Jon Kleinberg
There is inherent information captured in the order in which we write words in a list. The orderings of binomials --- lists of two words separated by `and' or `or' --- has been stu…
Clustering in graphs and hypergraphs with categorical edge labels
Ilya Amburg, Nate Veldt, Austin R. Benson
Modern graph or network datasets often contain rich structure that goes beyond simple pairwise connections between nodes. This calls for complex representations that can capture, f…
Retrieving Top Weighted Triangles in Graphs
Raunak Kumar, Paul Liu, Moses Charikar +1
Pattern counting in graphs is a fundamental primitive for many network analysis tasks, and a number of methods have been developed for scaling subgraph counting to large graphs. Ma…
Pairwise Link Prediction
Huda Nassar, Austin R. Benson, David F. Gleich
Link prediction is a common problem in network science that transects many disciplines. The goal is to forecast the appearance of new links or to find links missing in the network.…
Network Density of States
Kun Dong, Austin R. Benson, David Bindel
Spectral analysis connects graph structure to the eigenvalues and eigenvectors of associated matrices. Much of spectral graph theory descends directly from spectral geometry, the s…
Planted Hitting Set Recovery in Hypergraphs
Ilya Amburg, Jon Kleinberg, Austin R. Benson
In various application areas, networked data is collected by measuring interactions involving some specific set of core nodes. This results in a network dataset containing the core…
Measuring Directed Triadic Closure with Closure Coefficients
Hao Yin, Austin R. Benson, Johan Ugander
Recent work studying triadic closure in undirected graphs has drawn attention to the distinction between measures that focus on the "center" node of a wedge (i.e., length-2 path) v…
Network interpolation
Thomas Reeves, Anil Damle, Austin R. Benson
Given a set of snapshots from a temporal network we develop, analyze, and experimentally validate a so-called network interpolation scheme. Our method allows us to build a plausibl…
Modeling and Analysis of Tagging Networks in Stack Exchange Communities
Xiang Fu, Shangdi Yu, Austin R. Benson
Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web. While researchers have studied user behavior on the platforms in a variety of contexts, the…
Link Prediction in Networks with Core-Fringe Data
Austin R. Benson, Jon Kleinberg
Data collection often involves the partial measurement of a larger system. A common example arises in collecting network data: we often obtain network datasets by recording all of…
Choosing to Grow a Graph: Modeling Network Formation as Discrete Choice
Jan Overgoor, Austin R. Benson, Johan Ugander
We provide a framework for modeling social network formation through conditional multinomial logit models from discrete choice and random utility theory, in which each new edge is…
A sampling framework for counting temporal motifs
Paul Liu, Austin Benson, Moses Charikar
Pattern counting in graphs is fundamental to network science tasks, and there are many scalable methods for approximating counts of small patterns, often called motifs, in large gr…
Random Spatial Network Models with Core-Periphery Structure
Junteng Jia, Austin R. Benson
Core-periphery structure is a common property of complex networks, which is a composition of tightly connected groups of core vertices and sparsely connected periphery vertices. Th…