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MGAE: marginalized graph autoencoder for graph clustering
Graph clustering aims to discover community structures in networks, the task being fundamentally challenging mainly because the …
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Universal network representation for heterogeneous information networks
Network representation aims to represent the nodes in a network as continuous and compact vectors, and has attracted much attention in …
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Co-clustering enterprise social networks
An enterprise social network (ESN) involves diversified user groups from producers, suppliers, logistics, to end consumers, and users …
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Direct discriminative bag mapping for multi-instance learning
Multi-instance learning (MIL) is useful for tackling labeling ambiguity in learning tasks, by allowing a bag of instances to share one …
Iterative views agreement: an iterative low-rank based structured optimization method to multi-view spectral clustering
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their …
Tri-party deep network representation
Information network mining often requires examination of linkage relationships between nodes for analysis. Recently, network …
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Mining top-k minimal redundancy frequent patterns over uncertain databases
Frequent pattern mining from uncertain data has been paid closed attention due to most of the real life databases contain data with …
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Multi-graph-view learning for complicated object classification
In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a …
Multi-graph-view Learning for Graph Classification
Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to …
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Attribute weighting: how and when does it work for Bayesian Network Classification
A Bayesian Network (BN) is a graphical model which can be used to represent conditional dependency between random variables, such as …
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