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20172023
most citedGAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

96 citations · 557 across the 42 of their papers we have counts for

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10 papers · 1 filter

cs.SI20202 cited

GCN for HIN via Implicit Utilization of Attention and Meta-paths

Di Jin, Zhizhi Yu, Dongxiao He +3

Heterogeneous information network (HIN) embedding, aiming to map the structure and semantic information in a HIN to distributed representations, has drawn considerable research att…

cs.SI2020

Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark

Carl Yang, Yuxin Xiao, Yu Zhang +2

Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic super…

cs.SI2020

cube2net: Efficient Query-Specific Network Construction with Data Cube Organization

Carl Yang, Mengxiong Liu, Frank He +2

Networks are widely used to model objects with interactions and have enabled various downstream applications. However, in the real world, network mining is often done on particular…

cs.SI20194 cited

Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders

Carl Yang, Jieyu Zhang, Haonan Wang +5

While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i.e., social relations. Ideal edge semant…

cs.SI20196 cited

CubeNet: Multi-Facet Hierarchical Heterogeneous Network Construction, Analysis, and Mining

Carl Yang, Dai Teng, Siyang Liu +8

Due to the ever-increasing size of data, construction, analysis and mining of universal massive networks are becoming forbidden and meaningless. In this work, we outline a novel fr…

cs.SI2019

Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery

Carl Yang, Mengxiong Liu, Frank He +3

Heterogeneous networks are widely used to model real-world semi-structured data. The key challenge of learning over such networks is the modeling of node similarity under both netw…