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Found 22 papers, 5 papers with code
A model for efficient dynamical ranking in networks
We present a physics-inspired method for inferring dynamic rankings in directed temporal networks - networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction.
Effective Resistance for Pandemics: Mobility Network Sparsification for High-Fidelity Epidemic Simulation
One way to reduce the computational cost of simulating epidemics on these networks is sparsification, where a representative subset of edges is selected based on some measure of their importance.
Belief propagation for permutations, rankings, and partial orders
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.
Reconstruction of Random Geometric Graphs: Breaking the Omega(r) distortion barrier
We give an algorithm that, if $r=n^\alpha$ for any $\alpha > 0$, with high probability reconstructs the vertex positions with a maximum error of $O(n^\beta)$ where $\beta=1/2-(4/3)\alpha$, until $\alpha \ge 3/8$ where $\beta=0$ and the error becomes $O(\sqrt{\log n})$.
The Planted Matching Problem: Phase Transitions and Exact Results
We study the problem of recovering a planted matching in randomly weighted complete bipartite graphs $K_{n, n}$.
The Kikuchi Hierarchy and Tensor PCA
Our hierarchy is analogous to the sum-of-squares (SOS) hierarchy but is instead inspired by statistical physics and related algorithms such as belief propagation and AMP (approximate message passing).
A physical model for efficient ranking in networks
We present a physically-inspired model and an efficient algorithm to infer hierarchical rankings of nodes in directed networks.
The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness
While there are many ways to formalize it, one of the most popular is as an inference problem, where there is a "ground truth" community structure built into the graph somehow.
Computational Complexity Statistical Mechanics Social and Information Networks Probability Physics and Society
Community detection, link prediction, and layer interdependence in multilayer networks
In particular, this allows us to bundle layers together to compress redundant information, and identify small groups of layers which suffice to predict the remaining layers accurately.
Social and Information Networks Statistical Mechanics Physics and Society
Accurate and scalable social recommendation using mixed-membership stochastic block models
With ever-increasing amounts of online information available, modeling and predicting individual preferences-for books or articles, for example-is becoming more and more important.
Detectability thresholds and optimal algorithms for community structure in dynamic networks
We study the fundamental limits on learning latent community structure in dynamic networks.
Phase transitions in semisupervised clustering of sparse networks
For larger $k$ where a hard but detectable regime exists, we find that the easy/hard transition (the point at which efficient algorithms can do better than chance) becomes a line of transitions where the accuracy jumps discontinuously at a critical value of $\alpha$.
Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity
We address this problem by using the modularity as a Hamiltonian at finite temperature, and using an efficient Belief Propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it.
Phase Transitions in Community Detection: A Solvable Toy Model
It predicts a first-order detectability transition whenever $q > 2$, while the finite-temperature cavity method shows that this is the case only when $q > 4$.
Spectral redemption: clustering sparse networks
Spectral algorithms are classic approaches to clustering and community detection in networks.
Scalable Text and Link Analysis with Mixed-Topic Link Models
The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm.
Model Selection for Degree-corrected Block Models
We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs.
Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
In this paper we extend our previous work on the stochastic block model, a commonly used generative model for social and biological networks, and the problem of inferring functional groups or communities from the topology of the network.
Statistical Mechanics Disordered Systems and Neural Networks Social and Information Networks Physics and Society
Phase transition in the detection of modules in sparse networks
We present an asymptotically exact analysis of the problem of detecting communities in sparse random networks.
Finding community structure in very large networks
Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(m d log n) where d is the depth of the dendrogram describing the community structure.
Statistical Mechanics Disordered Systems and Neural Networks
Glassy dynamics and aging in an exactly solvable spin model
Instead, it falls out of equilibrium at a temperature which decreases logarithmically as a function of the cooling time.
Statistical Mechanics
