no code implementations • 23 Apr 2024 • Hao Miao, Senzhang Wang, Meiyue Zhang, Diansheng Guo, Funing Sun, Fan Yang
In this paper, we study the novel problem of multi-channel traffic flow prediction, and propose a deep \underline{M}ulti-\underline{V}iew \underline{C}hannel-wise \underline{S}patio-\underline{T}emporal \underline{Net}work (MVC-STNet) model to effectively address it.
no code implementations • 7 Mar 2024 • Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang
To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.
no code implementations • 26 Feb 2024 • Peiyan Zhang, Chaozhuo Li, Liying Kang, Feiran Huang, Senzhang Wang, Xing Xie, Sunghun Kim
Moreover, we show that existing contrastive objective learns the low-frequency component of the augmentation graph and propose a high-frequency component (HFC)-aware contrastive learning objective that makes the learned embeddings more distinctive.
no code implementations • 12 Feb 2024 • Yijie Zhang, Yuanchen Bei, Hao Chen, Qijie Shen, Zheng Yuan, Huan Gong, Senzhang Wang, Feiran Huang, Xiao Huang
POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG.
1 code implementation • 26 Jan 2024 • Hao Chen, Yuanchen Bei, Qijie Shen, Yue Xu, Sheng Zhou, Wenbing Huang, Feiran Huang, Senzhang Wang, Xiao Huang
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors.
1 code implementation • 6 Nov 2023 • Dongcheng Zou, Senzhang Wang, Xuefeng Li, Hao Peng, Yuandong Wang, Chunyang Liu, Kehua Sheng, Bo Zhang
Based on this, we propose a relative structural entropy-based position encoding and a multi-head attention masking scheme based on multi-layer encoding trees.
no code implementations • 1 Nov 2023 • Jiangnan Xia, Yu Yang, Senzhang Wang, Hongzhi Yin, Jiannong Cao, Philip S. Yu
To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.
no code implementations • 5 Sep 2023 • Shunyang Zhang, Senzhang Wang, Xianzhen Tan, Ruochen Liu, Jian Zhang, Jianxin Wang
Spatial time series imputation is critically important to many real applications such as intelligent transportation and air quality monitoring.
no code implementations • 28 Aug 2023 • Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Sunghun Kim
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs.
1 code implementation • 23 May 2023 • Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, Sunghun Kim
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
1 code implementation • ECML-PKDD 2023 • Sen Zhang, Senzhang Wang, Xiang Wang, Shigeng Zhang, Hao Miao & Junxing Zhu
We first project users and trajectories into the common latent feature space through learning a projection function (generator) to minimize the distance between the user distribution and the trajectory distribution.
1 code implementation • 8 Oct 2022 • Yaohua Wang, Fangyi Zhang, Ming Lin, Senzhang Wang, Xiuyu Sun, Rong Jin
A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges.
no code implementations • 2 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.
1 code implementation • 18 May 2022 • Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jae Boum Kim, Kai Zhang, Senzhang Wang, Xing Xie, Sunghun Kim
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions.
no code implementations • 10 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.
1 code implementation • 16 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.
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.
1 code implementation • 13 Dec 2021 • Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jaeboum Kim, Kai Zhang, Senzhang Wang, Sunghun Kim
Our approach leverages bidirectional temporal augmentation and knowledge-enhanced fine-tuning to synthesize authentic pseudo-prior items that \emph{retain user preferences and capture deeper item semantic correlations}, thus boosting the model's expressive power.
1 code implementation • 15 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.
no code implementations • 12 Aug 2021 • Jinpeng Chen, Haiyang Li, Xudong Zhang, Fan Zhang, Senzhang Wang, Kaimin Wei, Jiaqi Ji
The current studies generally learn user preferences according to the transitions of items in the user's session sequence.
1 code implementation • 22 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.
no code implementations • 30 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.
1 code implementation • 9 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.
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.
no code implementations • 26 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.
no code implementations • 15 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.
no code implementations • 15 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.
1 code implementation • 12 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.
no code implementations • 14 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.
no code implementations • 10 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.
no code implementations • 11 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.
1 code implementation • 9 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.
1 code implementation • 9 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.
no code implementations • 9 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.
no code implementations • 7 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.
1 code implementation • 3 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\%.
no code implementations • 11 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.
no code implementations • 2 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.