119 citations · 607 across the 16 of their papers we have counts for
17 papers
Graph neural networks and the energetic cavity method for combinatorial optimization
Joe Bacchus George, George T. Cantwell
We study the use of graph neural networks (GNNs) for finding approximate ground states of Ising models. Efficiently finding these ground states is of broad significance because man…
Model inference for ranking from pairwise comparisons
Daniel Sánchez Catalina, George T. Cantwell
We consider the problem of ranking objects from noisy pairwise comparisons, for example, ranking tennis players from the outcomes of matches. We follow a standard approach to this…
Embedding networks with the random walk first return time distribution
Vedanta Thapar, Renaud Lambiotte, George T. Cantwell
We propose the first return time distribution (FRTD) of a random walk as an interpretable and mathematically grounded node embedding. The FRTD assigns a probability mass function t…
An approximation for return time distributions of random walks on sparse networks
Erik Hormann, Renaud Lambiotte, George T. Cantwell
We propose an approximation for the first return time distribution of random walks on undirected networks. We combine a message-passing solution with a mean-field approximation, to…
Heterogeneous message passing for heterogeneous networks
George T. Cantwell, Alec Kirkley, Filippo Radicchi
Message passing (MP) is a computational technique used to find approximate solutions to a variety of problems defined on networks. MP approximations are generally accurate in local…
Approximate sampling and estimation of partition functions using neural networks
George T. Cantwell
We consider the closely related problems of sampling from a distribution known up to a normalizing constant, and estimating said normalizing constant. We show how variational autoe…
Belief propagation for permutations, rankings, and partial orders
George T. Cantwell, Cristopher Moore
Many datasets give partial information about an ordering or ranking by indicating which team won a game, which item a user prefers, or who infected whom. We define a continuous spi…
The friendship paradox in real and model networks
George T. Cantwell, Alec Kirkley, M. E. J. Newman
The friendship paradox is the observation that the degrees of the neighbors of a node in any network will, on average, be greater than the degree of the node itself. In common parl…
Belief propagation for networks with loops
Alec Kirkley, George T. Cantwell, M. E. J. Newman
Belief propagation is a widely used message passing method for the solution of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical models,…
Bayesian inference of network structure from unreliable data
Jean-Gabriel Young, George T. Cantwell, M. E. J. Newman
Most empirical studies of complex networks do not return direct, error-free measurements of network structure. Instead, they typically rely on indirect measurements that are often…
Inference for growing trees
George T. Cantwell, Guillaume St-Onge, Jean-Gabriel Young
One can often make inferences about a growing network from its current state alone. For example, it is generally possible to determine how a network changed over time or pick among…
Improved mutual information measure for classification and community detection
M. E. J. Newman, George T. Cantwell, Jean-Gabriel Young
The information theoretic quantity known as mutual information finds wide use in classification and community detection analyses to compare two classifications of the same set of o…
Message passing on networks with loops
George T. Cantwell, M. E. J. Newman
In this paper we offer a solution to a long-standing problem in the study of networks. Message passing is a fundamental technique for calculations on networks and graphs. The first…
Thresholding normally distributed data creates complex networks
George T. Cantwell, Yanchen Liu, Benjamin F. Maier +4
Network data sets are often constructed by some kind of thresholding procedure. The resulting networks frequently possess properties such as heavy-tailed degree distributions, clus…
Mixing patterns and individual differences in networks
George T. Cantwell, M. E. J. Newman
We study mixing patterns in networks, meaning the propensity for nodes of different kinds to connect to one another. The phenomenon of assortative mixing, whereby nodes prefer to c…
Balance in signed networks
Alec Kirkley, George T. Cantwell, M. E. J. Newman
We consider signed networks in which connections or edges can be either positive (friendship, trust, alliance) or negative (dislike, distrust, conflict). Early literature in graph…
Efficient method for estimating the number of communities in a network
Maria A. Riolo, George T. Cantwell, Gesine Reinert +1
While there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here w…