arXiv research

We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capt…

TIP model improves POSE prediction with less resources.

Unified model combines feature and label propagation for semi-supervised classification.

FlowGN tackles graph representation learning by tracing information flow paths.

The main approaches for node classification in graphs are information propagation and the association of the class of the node with external information. State of the art methods merge these approaches through Graph Convolutional Networks. We here use the association of topological features of the nodes with their clas…

Graph algorithms are key tools in many fields of science and technology. Some of these algorithms depend on propagating information between distant nodes in a graph. Recently, there have been a number of deep learning architectures proposed to learn on undirected graphs. However, most of these architectures aggregate i…

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 …

Propagation-regularization improves GNN performance by infusing extra graph information.

GHNet improves graph learning by balancing homogeneity and heterogeneity.

Improves label propagation for weakly supervised learning.

Unified model combines GCN and LPA for better node classification.

Neural network learns from higher-order connections in molecules.

Proposes a new method to optimize graph neural network architectures on heterogeneous information networks.

Combines neural networks and probabilistic graphical models for efficient higher-order inference.

This paper proposes the Mesh Neural Network (MNN), a novel architecture which allows neurons to be connected in any topology, to efficiently route information. In MNNs, information is propagated between neurons throughout a state transition function. State and error gradients are then directly computed from state updat…

Graph-based Bayesian SSL uses graph theory to propagate labels from a few to many unlabeled features.

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existi…

Generalized belief propagation converges to optimal solutions on graphs with motifs.

Bandit is a framework for designing sequential experiments. In each experiment, a learner selects an arm $A \in \mathcal{A}$ and obtains an observation corresponding to $A$. Theoretically, the tight regret lower-bound for the general bandit is polynomial with respect to the number of arms $|\mathcal{A}|$. This makes ba…

Graph neural networks struggle to propagate long-range information, causing over-squashing.

GraphShield uses dynamic graph learning to detect and visualize financial risks.

We perform theoretical and algorithmic studies for the problem of clustering and semi-supervised classification on graphs with both pairwise relational information and single-point feature information, upon a joint stochastic block model for generating synthetic graphs with both edges and node features. Asymptotically …

HyperSAGE learns node representations in hypergraphs without losing information.

To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying late…

Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues in the current ensemble clustering research. First, most of the existing algorithms tend to investigate the ensemble information at the obje…

Study reveals decurve flows in graph propagation models.

Improved error correction using neural networks and belief propagation.

A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heavy training on big data. Meta-learning addresses this problem by extracting common knowledge across different tasks that can be quickly adapt…

We study the effect of the quality and quantity of side information on the recovery of a hidden community of size $K=o(n)$ in a graph of size $n$. Side information for each node in the graph is modeled by a random vector with the following features: either the dimension of the vector is allowed to vary with $n$, while …

REST framework predicts stock trends by considering stock-specific and related-stock events.

New approach to deeper graph neural networks to avoid performance degradation.

ISP improves GNN expressivity by stratifying nodes based on graph invariants.

New $\alpha$-BP algorithm improves belief propagation for graphs with loops.

Proposes a method to select features for subgroup datasets with systematic missing data.

DimeNet uses directional message passing to improve molecular predictions.

Zero-shot and few-shot learning aim to improve generalization to unseen concepts, which are promising in many realistic scenarios. Due to the lack of data in unseen domain, relation modeling between seen and unseen domains is vital for knowledge transfer in these tasks. Most existing methods capture seen-unseen relatio…

Graphs avoid oversmoothing with properly initialized weights.

Graphs are essential representations of many real-world data such as social networks. Recent years have witnessed the increasing efforts made to extend the neural network models to graph-structured data. These methods, which are usually known as the graph neural networks, have been applied to advance many graphs relate…

Graph transformers outperform graph convolutions by preserving community information.

Improved text summarization using belief propagation on weighted bipartite graphs.

Improved few-shot learning with lower-level neural network embeddings.

Paper proposes KE-GCN for better graph embedding.

Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors. It is often the case that a node is not only influenc…

Belief propagation (BP) can do exact inference in loop-free graphs, but its performance could be poor in graphs with loops, and the understanding of its solution is limited. This work gives an interpretable belief propagation rule that is actually minimization of a localized $α$-divergence. We term this algorithm as $α…

A novel algorithm for unsupervised graph representation learning combining coarsening and mutual information maximization.

LNPE enhances local connections in embeddings using extended neighbor propagation.

A new neural network layer integrates graph learning into classification tasks.

Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propagating "soft labels" (e.g. probability distributions, class membership scores) over hypergraphs, by means of optimal transportation. Borrowin…