activity
20142022
most citedTransfer Learning across Networks for Collective Classification

72 citations · 220 across the 23 of their papers we have counts for

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Showing cs.SIShow all

7 papers · 1 filter

cs.SI2021★ 10 cited

User Response Prediction in Online Advertising

Zhabiz Gharibshah, Xingquan Zhu

Online advertising, as the vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The…

cs.SI2020★ 6 cited

Topology and Content Co-Alignment Graph Convolutional Learning

Min Shi, Yufei Tang, Xingquan Zhu

In traditional Graph Neural Networks (GNN), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learni…

cs.SI2019★ 1 cited

Search Efficient Binary Network Embedding

Daokun Zhang, Jie Yin, Xingquan Zhu +1

Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such tha…

cs.SI2019★ 4 cited

Attributed Network Embedding via Subspace Discovery

Daokun Zhang, Jie Yin, Xingquan Zhu +1

Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on netwo…

cs.SI2018★ 4 cited

SINE: Scalable Incomplete Network Embedding

Daokun Zhang, Jie Yin, Xingquan Zhu +1

Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content.…

cs.SI2018★ 2 cited

MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding

Daokun Zhang, Jie Yin, Xingquan Zhu +1

Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. As a result…

cs.SI2018★ 45 cited

Network Representation Learning: A Survey

Daokun Zhang, Jie Yin, Xingquan Zhu +1

With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as soc…