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Yuanxin Ning

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2 papers

1 author row

AAAI Conference 2020 Conference Paper

Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification

Hao Peng
Jianxin Li
Qiran Gong
Yuanxin Ning
Senzhang Wang
Lifang He

Graph classiﬁcation is critically important to many real-world applications that are associated with graph data such as chemical drug analysis and social network mining. Traditional methods usually require feature engineering to extract the graph features that can help discriminate the graphs of different classes. Although recently deep learning based graph embedding approaches are proposed to automatically learn graph features, they mostly use a few vertex arrangements extracted from the graph for feature learning, which may lose some structural information. In this work, we present a novel motif-based attentional graph convolution neural network for graph classiﬁcation, which can learn more discriminative and richer graph features. Speciﬁcally, a motif-matching guided subgraph normalization method is developed to better preserve the spatial information. A novel subgraph-level selfattention network is also proposed to capture the different impacts or weights of different subgraphs. Experimental results on both bioinformatics and social network datasets show that the proposed models signiﬁcantly improve graph classiﬁcation performance over both traditional graph kernel methods and recent deep learning approaches.

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IJCAI Conference 2019 Conference Paper

Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks

Hao Peng
Jianxin Li
Qiran Gong
Yangqiu Song
Yuanxin Ning
Kunfeng Lai
Philip S. Yu

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques.

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