69 citations · 336 across the 32 of their papers we have counts for
6 papers · 1 filter
Multilabel Consensus Classification
Sihong Xie, Xiangnan Kong, Jing Gao +2
In the era of big data, a large amount of noisy and incomplete data can be collected from multiple sources for prediction tasks. Combining multiple models or data sources helps to…
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks
Jiawei Zhang, Xiangnan Kong, Philip S. Yu
Online social networks have gained great success in recent years and many of them involve multiple kinds of nodes and complex relationships. Among these relationships, social links…
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
Chuan Shi, Xiangnan Kong, Yue Huang +2
Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type. However, in many scenarios, w…
Predicting Trends in Social Networks via Dynamic Activeness Model
Shuyang Lin, Xiangnan Kong, Philip S. Yu
With the effect of word-of-the-mouth, trends in social networks are now playing a significant role in shaping people's lives. Predicting dynamic trends is an important problem with…
Meta Path-Based Collective Classification in Heterogeneous Information Networks
Xiangnan Kong, Bokai Cao, Philip S. Yu +2
Collective classification has been intensively studied due to its impact in many important applications, such as web mining, bioinformatics and citation analysis. Collective classi…
Discriminative Feature Selection for Uncertain Graph Classification
Xiangnan Kong, Philip S. Yu, Xue Wang +1
Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, et…