136 citations · 553 across the 42 of their papers we have counts for
11 papers · 1 filter
Imbalanced Graph Classification via Graph-of-Graph Neural Networks
Yu Wang, Yuying Zhao, Neil Shah +1
Graph Neural Networks (GNNs) have achieved unprecedented success in identifying categorical labels of graphs. However, most existing graph classification problems with GNNs follow…
Attributed Graph Modeling with Vertex Replacement Grammars
Satyaki Sikdar, Neil Shah, Tim Weninger
Recent work at the intersection of formal language theory and graph theory has explored graph grammars for graph modeling. However, existing models and formalisms can only operate…
Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs
Hyeonjeong Shin, Taehyung Kwon, Neil Shah +1
A variety of tasks on dynamic graphs, including anomaly detection, community detection, compression, and graph understanding, have been formulated as problems of identifying consti…
Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation
Shichang Zhang, Yozen Liu, Yizhou Sun +1
Graph Neural Networks (GNNs) are popular for graph machine learning and have shown great results on wide node classification tasks. Yet, they are less popular for practical deploym…
Graph Condensation for Graph Neural Networks
Wei Jin, Lingxiao Zhao, Shichang Zhang +3
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, w…
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
Lingxiao Zhao, Wei Jin, Leman Akoglu +1
Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network (GNN), in which each node's representation is computed recursively by aggregating representations…
Is Homophily a Necessity for Graph Neural Networks?
Yao Ma, Xiaorui Liu, Neil Shah +1
Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node cla…
Automated Self-Supervised Learning for Graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao +3
Graph self-supervised learning has gained increasing attention due to its capacity to learn expressive node representations. Many pretext tasks, or loss functions have been designe…
Sketch-Based Anomaly Detection in Streaming Graphs
Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi +3
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, usin…
KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection
Sara Abdali, Neil Shah, Evangelos E. Papalexakis
Graphs are one of the most efficacious structures for representing datapoints and their relations, and they have been largely exploited for different applications. Previously, the…
Identifying Misinformation from Website Screenshots
Sara Abdali, Rutuja Gurav, Siddharth Menon +4
Can the look and the feel of a website give information about the trustworthiness of an article? In this paper, we propose to use a promising, yet neglected aspect in detecting the…