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Found 22 papers, 16 papers with code
Topological structure of complex predictions
Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data.
A flexible PageRank-based graph embedding framework closely related to spectral eigenvector embeddings
We study a simple embedding technique based on a matrix of personalized PageRank vectors seeded on a random set of nodes.
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering
For this problem, we propose a novel generalization of random walk, diffusion, or smooth function methods in the literature to a convex p-norm cut function.
Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs
For a certain choice of parameters it is also related to our hypergraph objective.
Learning Resolution Parameters for Graph Clustering
We begin by formalizing the notion of a parameter fitness function, which measures how well a fixed input clustering approximately solves a generalized clustering objective for a specific resolution parameter value.
A Parallel Projection Method for Metric Constrained Optimization
In this paper we present a parallel projection method for metric-constrained optimization which allows us to speed up the convergence rate in practice.
A Short Introduction to Local Graph Clustering Methods and Software
Scalability problems led to the development of local graph clustering algorithms that come with a variety of theoretical guarantees.
Social and Information Networks
Low rank methods for multiple network alignment
While there are a large number of effective techniques for pairwise problems with two networks that scale in terms of edges, these cannot be readily extended to align multiple networks as the computational complexity will tend to grow exponentially with the number of networks. In this paper we introduce a new multiple network alignment algorithm and framework that is effective at aligning thousands of networks with thousands of nodes.
Coin-flipping, ball-dropping, and grass-hopping for generating random graphs from matrices of edge probabilities
Common models for random graphs, such as Erd\H{o}s-R\'{e}nyi and Kronecker graphs, correspond to generating random adjacency matrices where each entry is non-zero based on a large matrix of probabilities.
Social and Information Networks Discrete Mathematics Combinatorics
Deconvolving Feedback Loops in Recommender Systems
Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences.
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.
Correlation Clustering with Low-Rank Matrices
In this paper we explore how to solve the correlation clustering objective exactly when the data to be clustered can be represented by a low-rank matrix.
Multi-way Monte Carlo Method for Linear Systems
A sufficient condition for the method to work is $\| H \| < 1$, which greatly limits the usability of this method.
Fast Multiplier Methods to Optimize Non-exhaustive, Overlapping Clustering
In this paper, we consider two fast multiplier methods to accelerate the convergence of an augmented Lagrangian scheme: a proximal method of multipliers and an alternating direction method of multipliers (ADMM).
AptRank: An Adaptive PageRank Model for Protein Function Prediction on Bi-relational Graphs
Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood- and module-based methods.
Molecular Networks Social and Information Networks 92-08
Seeded PageRank Solution Paths
We study the behavior of network diffusions based on the PageRank random walk from a set of seed nodes.
Social and Information Networks 91D30 (Primary) I.5.3; G.1.3; F.2.2
A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares
We present a parallel algorithm for the undirected $s, t$-mincut problem with floating-point valued weights.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Numerical Analysis
PageRank beyond the Web
Google's PageRank method was developed to evaluate the importance of web-pages via their link structure.
Social and Information Networks Computational Engineering, Finance, and Science Numerical Analysis Physics and Society
Heat kernel based community detection
On a real-world community identification task, the heat kernel communities perform better than those from the PageRank diffusion.
Social and Information Networks Data Structures and Algorithms Physics and Society 91D30 (Primary) I.5.3
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.
Parallel Maximum Clique Algorithms with Applications to Network Analysis and Storage
We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks.
Social and Information Networks Distributed, Parallel, and Cluster Computing Discrete Mathematics Data Structures and Algorithms Physics and Society 05C69 G.2.2
