arXiv research

G-Meta learns graph meta-learning from local subgraphs.

Link prediction is a key problem for network-structured data. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihood of links. They have obtained wide practical uses due to their simplicity, interpretability, and for some of them, scalability. However, ev…

This paper proposes a unified framework to quantify local and global inferential uncertainty for high dimensional nonparanormal graphical models. In particular, we consider the problems of testing the presence of a single edge and constructing a uniform confidence subgraph. Due to the presence of unknown marginal trans…

Enhances GNNs to better capture local graph structures.

VISTA learns causal structures by integrating local subgraphs, improving accuracy and efficiency.

Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classificati…

PSimGNN partitions graphs into subgraphs for efficient graph similarity computation.

New dataset and techniques detect money laundering patterns in crypto.

The study proves sampling-based GNNs can approximate training on full graphs with small subgraphs.

SubGNN tackles subgraph prediction challenges in graphs.

Spectral algorithms solve optimal community detection and related problems.

The paper introduces subgraph nomination for finding similar subgraphs in networks.

NeuroMatch efficiently matches subgraphs in large graphs using neural networks.

Unified framework for subgraph-enhanced GNNs, improving prediction accuracy and reducing computation time.

Study area-minimizing subgraphs in integer lattices.

We propose graph kernels based on subgraph matchings, i.e. structure-preserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al., 2009) in general can not be applied to attributed graphs, our approach allows to rate mappings of subgraphs by …

GNNS uses graph neural networks to efficiently estimate subgraph frequency distributions.

We prove several results about chordal graphs and weighted chordal graphs by focusing on exposed edges. These are edges that are properly contained in a single maximal complete subgraph. This leads to a characterization of chordal graphs via deletions of a sequence of exposed edges from a complete graph. Most interesti…

Proposes GIB for recognizing informative subgraphs in graphs.

FSD-CAP improves graph feature imputation under high missing rates.

Learning representation on graph plays a crucial role in numerous tasks of pattern recognition. Different from grid-shaped images/videos, on which local convolution kernels can be lattices, however, graphs are fully coordinate-free on vertices and edges. In this work, we propose a Gaussian-induced convolution (GIC) fra…

We consider the densest $k$-subgraph problem, which seeks to identify the $k$-node subgraph of a given input graph with maximum number of edges. This problem is well-known to be NP-hard, by reduction to the maximum clique problem. We propose a new convex relaxation for the densest $k$-subgraph problem, based on a nucle…

RevTrack identifies suspicious subgraphs on blockchain for AML.

Faster algorithm for generalized mean densest subgraph problem.

Let $Γ$ be a finite graph and let $Γ^{\mathrm{e}}$ be its extension graph. We inductively define a sequence $\{Γ_i\}$ of finite induced subgraphs of $Γ^{\mathrm{e}}$ through successive applications of an operation called "doubling along a star". Then we show that every finite induced subgraph of $Γ^{\mathrm{e}}$ is iso…

Classification and regression in which the inputs are graphs of arbitrary size and shape have been paid attention in various fields such as computational chemistry and bioinformatics. Subgraph indicators are often used as the most fundamental features, but the number of possible subgraph patterns are intractably large …

We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we …

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information …

Estimates eigenvalues of poly-Laplace operator on lattice subgraphs.

Efficiently matches subgraphs in noisy data without node labels.

ESAN improves graph neural networks by processing subgraphs.

Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objec…

Subg-Con learns graph representations from subgraphs, improving scalability and efficiency.

The ability to detect and count certain substructures in graphs is important for solving many tasks on graph-structured data, especially in the contexts of computational chemistry and biology as well as social network analysis. Inspired by this, we propose to study the expressive power of graph neural networks (GNNs) v…

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting is NP-complete and requires more global inference to oversee the whole graph. To make it …

We present a supervised-learning algorithm from graph data (a set of graphs) for arbitrary twice-differentiable loss functions and sparse linear models over all possible subgraph features. To date, it has been shown that under all possible subgraph features, several types of sparse learning, such as Adaboost, LPBoost, …

Upper bounds for Steklov eigenvalues in subgraphs of polynomial growth Cayley graphs.

GMT improves interpretability of XGNNs by approximating SubMT.

Introduces a new manifold from a graph subgraph.

SELO model predicts link signs better than SDGNN using subgraph encoding and linear optimization.

New method linearizes nonlinear coupled oscillators on graphs.

The success of graph embeddings or node representation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim to preserve local and global network structure by identifying no…

Neural network for subgraph similarity computation with pruning.

Cliques, or fully connected subgraphs, are among the most important and well-studied graph motifs in network science. We consider the problem of finding a statisti- cally anomalous clique hidden in a large network. There are two parts to this problem: (1) detection, i.e., determining whether an anomalous clique is pres…

Many real world graphs, such as the graphs of molecules, exhibit structure at multiple different scales, but most existing kernels between graphs are either purely local or purely global in character. In contrast, by building a hierarchy of nested subgraphs, the Multiscale Laplacian Graph kernels (MLG kernels) that we …

Metric graphs have subgraphs with entropy at least λ.

The study extends Tutte's conflict graph concept to nonplanar graphs.

Finite subgraphs in flip graphs ensure unique surface embeddings.