51 citations · 134 across the 7 of their papers we have counts for
8 papers
Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems
Yixin Su, Yunxiang Zhao, Sarah Erfani +2
Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions. However, the…
Edge-based Local Push for Personalized PageRank
Hanzhi Wang, Zhewei Wei, Junhao Gan +3
Personalized PageRank (PPR) is a popular node proximity metric in graph mining and network research. Given a graph G=(V,E) and a source node , a single-source PPR (SSPPR)…
Dynamic Structural Clustering on Graphs
Boyu Ruan, Junhao Gan, Hao Wu +1
Structural Clustering () is one of the most popular graph clustering paradigms. In this paper, we consider under two commonly adapted similarities, namely Jaccard…
Neural Graph Matching based Collaborative Filtering
Yixin Su, Rui Zhang, Sarah Erfani +1
User and item attributes are essential side-information; their interactions (i.e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various…
Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push
Hao Wu, Junhao Gan, Zhewei Wei +1
Personalized PageRank (PPR) is a critical measure of the importance of a node t to a source node s in a graph. The Single-Source PPR (SSPPR) query computes the PPR's of all the nod…
Personalized PageRank to a Target Node, Revisited
Hanzhi Wang, Zhewei Wei, Junhao Gan +2
Personalized PageRank (PPR) is a widely used node proximity measure in graph mining and network analysis. Given a source node and a target node , the PPR value repr…