Search Results for author: Senzhang Wang

Found 24 papers, 9 papers with code

Geometric Interaction Augmented Graph Collaborative Filtering

no code implementations2 Aug 2022 Yiding Zhang, Chaozhuo Li, Senzhang Wang, Jianxun Lian, Xing Xie

Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests.

Collaborative Filtering

Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks

no code implementations10 May 2022 Jiaqiang Zhang, Senzhang Wang, Songcan Chen

Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security.

Anomaly Detection Contrastive Learning +2

HousE: Knowledge Graph Embedding with Householder Parameterization

1 code implementation16 Feb 2022 Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang

The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.

Knowledge Graph Embedding Relation Mapping

Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space

2 code implementations ICLR 2022 Yaohua Wang, Yaobin Zhang, Fangyi Zhang, Ming Lin, Yuqi Zhang, Senzhang Wang, Xiuyu Sun

In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images.

Face Clustering

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

1 code implementation15 Oct 2021 Xingcheng Fu, JianXin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu

Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.

Graph Learning Multi-agent Reinforcement Learning +1

Session-based Recommendation with Heterogeneous Graph Neural Network

no code implementations12 Aug 2021 Jinpeng Chen, Haiyang Li, Fan Zhang, Senzhang Wang, Kaimin Wei

The current studies generally learn user preferences according to the transitions of items in the user's session sequence.

Session-Based Recommendations

A Robust and Generalized Framework for Adversarial Graph Embedding

1 code implementation22 May 2021 JianXin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun Sun, Philip S. Yu, Lifang He

With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.

Graph Embedding Graph Mining +3

Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks

no code implementations30 Aug 2020 Qingyun Sun, Hao Peng, Jian-Xin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S. Yu, Lifang He

Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.

Graph Embedding

Adversarial Directed Graph Embedding

1 code implementation9 Aug 2020 Shijie Zhu, JianXin Li, Hao Peng, Senzhang Wang, Lifang He

To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.

Graph Embedding Graph Mining

Decode with Template: Content Preserving Sentiment Transfer

no code implementations LREC 2020 Zhiyuan Wen, Jiannong Cao, Ruosong Yang, Senzhang Wang

The two major challenges in existing works lie in (1) effectively disentangling the original sentiment from input sentences; and (2) preserving the semantic content while transferring the sentiment.

Interpretable Deep Learning Model for Online Multi-touch Attribution

no code implementations26 Mar 2020 Dongdong Yang, Kevin Dyer, Senzhang Wang

DeepMTA mainly contains two parts, the phased-LSTMs based conversion prediction model to catch different time intervals, and the additive feature attribution model combined with shaley values.


SOM-based DDoS Defense Mechanism using SDN for the Internet of Things

no code implementations15 Mar 2020 Yunfei Meng, Zhiqiu Huang, Senzhang Wang, Guohua Shen, Changbo Ke

To effectively tackle the security threats towards the Internet of things, we propose a SOM-based DDoS defense mechanism using software-defined networking (SDN) in this paper.

DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage

no code implementations15 Oct 2019 Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang

The source code suggestions provided by current IDEs are mostly dependent on static type learning.

Code Completion

Deep Transfer Learning for Source Code Modeling

1 code implementation12 Oct 2019 Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang

A challenging issue of these approaches is that they require training from starch for a different related problem.

Transfer Learning

Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

no code implementations14 Sep 2019 Chuan Shi, Xiaotian Han, Li Song, Xiao Wang, Senzhang Wang, Junping Du, Philip S. Yu

However, the characteristics of users and the properties of items may stem from different aspects, e. g., the brand-aspect and category-aspect of items.

Collaborative Filtering Recommendation Systems

Label-Aware Graph Convolutional Networks

no code implementations10 Jul 2019 Hao Chen, Yue Xu, Feiran Huang, Zengde Deng, Wenbing Huang, Senzhang Wang, Peng He, Zhoujun Li

In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models.

Classification General Classification +3

Deep Learning for Spatio-Temporal Data Mining: A Survey

no code implementations11 Jun 2019 Senzhang Wang, Jiannong Cao, Philip S. Yu

Next we classify existing literatures based on the types of ST data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience.

Anomaly Detection Management +1

Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Hao Yan, Qiran Gong, Senzhang Wang, Lin Liu, Lihong Wang, Xiang Ren

Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario.

Link Prediction Multi-Label Classification +1

Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Lifang He, Bo Li, Lihong Wang, Philip S. Yu

In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.

Classification General Classification +4

Mutual Clustering on Comparative Texts via Heterogeneous Information Networks

no code implementations9 Mar 2019 Jianping Cao, Senzhang Wang, Danyan Wen, Zhaohui Peng, Philip S. Yu, Fei-Yue Wang

HINT first models multi-sourced texts (e. g. news and tweets) as heterogeneous information networks by introducing the shared ``anchor texts'' to connect the comparative texts.

Text Clustering Transfer Learning

Multi-Hot Compact Network Embedding

no code implementations7 Mar 2019 Chaozhuo Li, Senzhang Wang, Philip S. Yu, Zhoujun Li

Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features.

Network Embedding

CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code Modeling

1 code implementation3 Mar 2019 Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang

We evaluate CodeGRU with real-world data set and it shows that CodeGRU outperforms the state-of-the-art language models and help reduce the vocabulary size up to 24. 93\%.

Code Completion Code Generation +1

Graph Convolutional Neural Networks via Motif-based Attention

no code implementations11 Nov 2018 Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Yuanxing Ning, Philip S. Yu

Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information.

General Classification Graph Classification

Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data

no code implementations2 Sep 2018 Xi Zhang, Yixuan Li, Senzhang Wang, Binxing Fang, Philip S. Yu

In this work, we study how to explore multiple data sources to improve the performance of the stock prediction.

Stock Prediction

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