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Found 40 papers, 34 papers with code
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs due to their impressive capability of capturing nonlinear relations in graph-structured data.
Graph-Based Methods for Discrete Choice
We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit.
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components
We develop the first approximation algorithms for this problem, where the approximations can be quickly computed via reduction to a sparse graph cut problem, with graph sparsity controlled by the desired approximation factor.
Edge Proposal Sets for Link Prediction
Here, we demonstrate how simply adding a set of edges, which we call a \emph{proposal set}, to the graph as a pre-processing step can improve the performance of several link prediction algorithms.
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Link Property Prediction
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Graph Belief Propagation Networks
With the wide-spread availability of complex relational data, semi-supervised node classification in graphs has become a central machine learning problem.
The Generalized Mean Densest Subgraph Problem
Finding dense subgraphs of a large graph is a standard problem in graph mining that has been studied extensively both for its theoretical richness and its many practical applications.
Choice Set Confounding in Discrete Choice
Standard methods in preference learning involve estimating the parameters of discrete choice models from data of selections (choices) made by individuals from a discrete set of alternatives (the choice set).
A nonlinear diffusion method for semi-supervised learning on hypergraphs
Hypergraphs are a common model for multiway relationships in data, and hypergraph semi-supervised learning is the problem of assigning labels to all nodes in a hypergraph, given labels on just a few nodes.
Generative hypergraph clustering: from blockmodels to modularity
Many graph algorithms for this task are based on variants of the stochastic blockmodel, a random graph with flexible cluster structure.
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations
Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning.
Over-parametrized neural networks as under-determined linear systems
We draw connections between simple neural networks and under-determined linear systems to comprehensively explore several interesting theoretical questions in the study of neural networks.
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
Node Classification on Non-Homophilic (Heterophilic) Graphs
Node Property Prediction
Learning Interpretable Feature Context Effects in Discrete Choice
Using our models, we identify new context effects in widely used choice datasets and provide the first analysis of choice set context effects in social network growth.
Communication-efficient distributed eigenspace estimation
Spectral methods are a collection of such problems, where solutions are orthonormal bases of the leading invariant subspace of an associated data matrix, which are only unique up to rotation and reflections.
Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform
For example, expertise on song annotations follows a "U shape" where experts are both early and late contributors with non-experts contributing intermediately; we develop a user utility model that captures such behavior.
Hypergraph Clustering for Finding Diverse and Experienced Groups
In contrast to related problems on fair or balanced clustering, we model diversity in terms of variety of past experience (instead of, e. g., protected attributes), with a goal of forming groups that have both experience and diversity with respect to participation in edge types.
Nonlinear Higher-Order Label Spreading
Label spreading is a general technique for semi-supervised learning with point cloud or network data, which can be interpreted as a diffusion of labels on a graph.
Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text
These binomials are common across many areas of speech, in both formal and informal text.
Minimizing Localized Ratio Cut Objectives in Hypergraphs
However, there are only a few specialized approaches for localized clustering in hypergraphs.
Entrywise convergence of iterative methods for eigenproblems
Several problems in machine learning, statistics, and other fields rely on computing eigenvectors.
Residual Correlation in Graph Neural Network Regression
A graph neural network transforms features in each vertex's neighborhood into a vector representation of the vertex.
Choice Set Optimization Under Discrete Choice Models of Group Decisions
The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set.
Clustering in graphs and hypergraphs with categorical edge labels
Here, we develop a computational framework for the problem of clustering hypergraphs with categorical edge labels --- or different interaction types --- where clusters corresponds to groups of nodes that frequently participate in the same type of interaction.
Neural Jump Stochastic Differential Equations
Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events.
Network Density of States
Much of spectral graph theory descends directly from spectral geometry, the study of differentiable manifolds through the spectra of associated differential operators.
Social and Information Networks Numerical Analysis
Graph-based Semi-Supervised & Active Learning for Edge Flows
The first strategy selects edges to minimize the reconstruction error bound and works well on flows that are approximately divergence-free.
Planted Hitting Set Recovery in Hypergraphs
In various application areas, networked data is collected by measuring interactions involving some specific set of core nodes.
Modeling and Analysis of Tagging Networks in Stack Exchange Communities
Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web.
Link Prediction in Networks with Core-Fringe Data
However, we find that this is not true; in fact, there is substantial variability in the value of the fringe nodes for prediction.
Detecting Core-Periphery Structure in Spatial Networks
The core-periphery structure, which decompose a network into a densely-connected core and a sparsely-connected periphery, constantly emerges from spatial networks such as traffic, biological and social networks.
Social and Information Networks Physics and Society
Three hypergraph eigenvector centralities
Eigenvector centrality is a standard network analysis tool for determining the importance of (or ranking of) entities in a connected system that is represented by a graph.
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian
Simplicial complexes, a mathematical object common in topological data analysis, have emerged as a model for multi-nodal interactions that occur in several complex systems; for example, biological interactions occur between a set of molecules rather than just two, and communication systems can have group messages and not just person-to-person messages.
Social and Information Networks Discrete Mathematics Algebraic Topology Physics and Society
Found Graph Data and Planted Vertex Covers
A typical way in which network data is recorded is to measure all the interactions among a specified set of core nodes; this produces a graph containing this core together with a potentially larger set of fringe nodes that have links to the core.
Simplicial Closure and higher-order link prediction
Networks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions.
Tools for higher-order network analysis
Networks are a fundamental model of complex systems throughout the sciences, and network datasets are typically analyzed through lower-order connectivity patterns described at the level of individual nodes and edges.
Higher-order clustering in networks
Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster.
Motifs in Temporal Networks
Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience.
Higher-order organization of complex networks
Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges.
Social and Information Networks Discrete Mathematics Physics and Society
General Tensor Spectral Co-clustering for Higher-Order Data
Spectral clustering and co-clustering are well-known techniques in data analysis, and recent work has extended spectral clustering to square, symmetric tensors and hypermatrices derived from a network.
Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices
We demonstrate the efficacy of these algorithms on terabyte-sized synthetic matrices and real-world matrices from scientific computing and bioinformatics.
