152 citations · 454 across the 15 of their papers we have counts for
15 papers
Neural PathSim for Inductive Similarity Search in Heterogeneous Information Networks
Wenyi Xiao, Huan Zhao, Vincent W. Zheng +1
PathSim is a widely used meta-path-based similarity in heterogeneous information networks. Numerous applications rely on the computation of PathSim, including similarity search and…
Differentially Private Federated Knowledge Graphs Embedding
Hao Peng, Haoran Li, Yangqiu Song +2
Knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-o…
Adam revisited: a weighted past gradients perspective
Hui Zhong, Zaiyi Chen, Chuan Qin +4
Adaptive learning rate methods have been successfully applied in many fields, especially in training deep neural networks. Recent results have shown that adaptive methods with expo…
Social Explorative Attention based Recommendation for Content Distribution Platforms
Wenyi Xiao, Huan Zhao, Haojie Pan +3
In modern social media platforms, an effective content recommendation should benefit both creators to bring genuine benefits to them and consumers to help them get really interesti…
Privacy Threats Against Federated Matrix Factorization
Dashan Gao, Ben Tan, Ce Ju +2
Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect suf…
Vertex-reinforced Random Walk for Network Embedding
Wenyi Xiao, Huan Zhao, Vincent W. Zheng +1
In this paper, we study the fundamental problem of random walk for network embedding. We propose to use non-Markovian random walk, variants of vertex-reinforced random walk (VRRW),…
Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
Min Hou, Le Wu, Enhong Chen +3
In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show prefere…
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
Wenyi Xiao, Huan Zhao, Haojie Pan +3
An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interestin…
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
Wei Yang, Wei Lu, Vincent W. Zheng
Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The…
Deep Transfer Learning for Cross-domain Activity Recognition
Jindong Wang, Vincent W. Zheng, Yiqiang Chen +1
Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve t…
Topological Recurrent Neural Network for Diffusion Prediction
Jia Wang, Vincent W. Zheng, Zemin Liu +1
In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an…
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang
Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behi…
Active Learning for Graph Embedding
Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the…
From Community Detection to Community Profiling
Hongyun Cai, Vincent W. Zheng, Fanwei Zhu +2
Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without…
From Node Embedding To Community Embedding
Vincent W. Zheng, Sandro Cavallari, Hongyun Cai +2
Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph a…