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Found 21 papers, 9 papers with code
p-Norm Flow Diffusion for Local Graph Clustering
Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes ranking and feature inference.
Exact Learning of Permutations for Nonzero Binary Inputs with Logarithmic Training Size and Quadratic Ensemble Complexity
The ability of an architecture to realize permutations is quite fundamental.
LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language Models
Counting is a fundamental operation for various visual tasks in real-life applications, requiring both object recognition and robust counting capabilities.
Positional Attention: Expressivity and Learnability of Algorithmic Computation
We analyze their in-distribution learnability and explore how parameter norms in positional attention affect sample complexity.
Analysis of Corrected Graph Convolutions
In this paper, we provide a rigorous theoretical analysis, based on the two-class contextual stochastic block model (CSBM), of the performance of vanilla graph convolution from which we remove the principal eigenvector to avoid oversmoothing.
Simulation of Graph Algorithms with Looped Transformers
The architecture we use is a looped transformer with extra attention heads that interact with the graph.
Local Graph Clustering with Noisy Labels
We provide sufficient conditions on the label noise under which, with high probability, using diffusion in the weighted graph yields a more accurate recovery of the target cluster.
Optimality of Message-Passing Architectures for Sparse Graphs
We study the node classification problem on feature-decorated graphs in the sparse setting, i. e., when the expected degree of a node is $O(1)$ in the number of nodes, in the fixed-dimensional asymptotic regime, i. e., the dimension of the feature data is fixed while the number of nodes is large.
On Classification Thresholds for Graph Attention with Edge Features
In CSBM the nodes and edge features are obtained from a mixture of Gaussians and the edges from a stochastic block model.
Effects of Graph Convolutions in Multi-layer Networks
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information.
Graph Attention Retrospective
They were introduced to allow a node to aggregate information from features of neighbor nodes in a non-uniform way, in contrast to simple graph convolution which does not distinguish the neighbors of a node.
Local Hyper-Flow Diffusion
Recently, hypergraphs have attracted a lot of attention due to their ability to capture complex relations among entities.
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Recently there has been increased interest in semi-supervised classification in the presence of graphical information.
General Classification
Out-of-Distribution Generalization
+1
$p$-Norm Flow Diffusion for Local Graph Clustering
Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes ranking and feature inference.
Statistical guarantees for local graph clustering
In this paper, we adopt a statistical perspective on local graph clustering, and we analyze the performance of the l1-regularized PageRank method~(Fountoulakis et.
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
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization
Parallel computing has played an important role in speeding up convex optimization methods for big data analytics and large-scale machine learning (ML).
LASAGNE: Locality And Structure Aware Graph Node Embedding
For larger graphs with flat NCPs that are strongly expander-like, existing methods lead to random walks that expand rapidly, touching many dissimilar nodes, thereby leading to lower-quality vector representations that are less useful for downstream tasks.
Capacity Releasing Diffusion for Speed and Locality.
As an application, we use our CRD Process to develop an improved local algorithm for graph clustering.
Capacity Releasing Diffusion for Speed and Locality
Thus, our CRD Process is the first local graph clustering algorithm that is not subject to the well-known quadratic Cheeger barrier.
A Randomized Rounding Algorithm for Sparse PCA
We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem.
