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20172024
most citedSelfish Sparse RNN Training

16 citations · 56 across the 25 of their papers we have counts for

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Showing cs.SIShow all

5 papers · 1 filter

cs.SI2021

The Banking Transactions Dataset and its Comparative Analysis with Scale-free Networks

Akrati Saxena, Yulong Pei, Jan Veldsink +3

We construct a network of 1.6 million nodes from banking transactions of users of Rabobank. We assign two weights on each edge, which are the aggregate transferred amount and the t…

cs.SI2021

A Survey on Role-Oriented Network Embedding

Pengfei Jiao, Xuan Guo, Ting Pan +2

Recently, Network Embedding (NE) has become one of the most attractive research topics in machine learning and data mining. NE approaches have achieved promising performance in var…

cs.SI2021★ 4 cited

Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks

Tianjin Huang, Yulong Pei, Vlado Menkovski +1

Recent years have witnessed an upsurge of interest in the problem of anomaly detection on attributed networks due to its importance in both research and practice. Although various…

cs.SI2018★ 1 cited

struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding

Yulong Pei, Xin Du, Jianpeng Zhang +2

Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitatio…

cs.SI2017

Introduction to a Temporal Graph Benchmark

Wouter Ligtenberg, Yulong Pei

A temporal graph is a data structure, consisting of nodes and edges in which the edges are associated with time labels. To analyze the temporal graph, the first step is to find a p…