Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining最新文献_第10页

A Semantic Search Framework for Similar Audit Issue Recommendation in Financial Industry 金融行业类似审计事项建议的语义搜索框架

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3573040

Chuchu Zhang, Can Song, Samarth Agarwal, Huayu Wu, Xuejie Zhang, John Jianan Lu

引用次数: 0

Generalizing Graph Neural Network across Graphs and Time 跨图和时间的广义图神经网络

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3572986

Zhi Wen

{"title":"Generalizing Graph Neural Network across Graphs and Time","authors":"Zhi Wen","doi":"10.1145/3539597.3572986","DOIUrl":"https://doi.org/10.1145/3539597.3572986","url":null,"abstract":"Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios require representations to be quickly generated for unseen nodes, new edges, or entirely new graphs. This inductive ability is essential for high-throughtput machine learning systems. However, this inductive graph representation problem is quite difficult, compared to the transductive setting, for that generalizing to unseen nodes requires new subgraphs containing the new nodes to be aligned to the neural network trained already. Meanwhile, following a message passing framework, graphneural network (GNN) is an inductive and powerful graph representation tool. We further explore inductive GNN from more specific perspectives: (1) generalizing GNN across graphs, in which we tackle with the problem of semi-supervised node classification across graphs; (2) generalizing GNN across time, in which we mainly solve the problem of temporal link prediction; (3) generalizing GNN across tasks; (4) generalizing GNN across locations.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121094795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Efficient Graph Learning for Anomaly Detection Systems 异常检测系统的高效图学习

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3572990

F. Febrinanto

引用次数: 0

Next-generation Challenges of Responsible Data Integration 负责任数据集成的下一代挑战

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3572727

F. Nargesian, Abolfazl Asudeh, H. Jagadish

引用次数: 1

Exploiting Explicit and Implicit Item relationships for Session-based Recommendation 为基于会话的推荐利用显式和隐式项目关系

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570432

Zihao Li, Xianzhi Wang, Chao Yang, L. Yao, Julian McAuley, Guandong Xu

{"title":"Exploiting Explicit and Implicit Item relationships for Session-based Recommendation","authors":"Zihao Li, Xianzhi Wang, Chao Yang, L. Yao, Julian McAuley, Guandong Xu","doi":"10.1145/3539597.3570432","DOIUrl":"https://doi.org/10.1145/3539597.3570432","url":null,"abstract":"The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily relies on graph structures, which are often predefined, task-specific, and designed heuristically. Furthermore, existing graph-based methods either neglect implicit correlations among items or consider explicit and implicit relationships altogether in the same graphs. We propose to decouple explicit and implicit relationships among items. As such, we can capture the prior knowledge encapsulated in explicit dependencies and learned implicit correlations among items simultaneously in a flexible and more interpretable manner for effective recommendations. We design a dual graph neural network that leverages the feature representations extracted by two GNNs: a graph neural network with a single gate (SG-GNN) and an adaptive graph neural network (A-GNN). The former models explicit dependencies among items. The latter employs a self-learning strategy to capture implicit correlations among items. Our experiments on four real-world datasets show our model outperforms state-of-the-art methods by a large margin, achieving 18.46% and 70.72% improvement in HR@20, and 49.10% and 115.29% improvement in MRR@20 on Diginetica and LastFM datasets.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127898705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Knowledge-Augmented Methods for Natural Language Processing 自然语言处理中的知识增强方法

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3572720

Chenguang Zhu, Yichong Xu, Xiang Ren, Bill Yuchen Lin, Meng Jiang, Wenhao Yu

引用次数: 12

SmartCityBus - A Platform for Smart Transportation Systems 智慧城市巴士——智慧交通系统的平台

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3575781

Georgios Bouloukakis, Chrysostomos Zeginis, N. Papadakis, K. Magoutis, George Christodoulou, Chrysanthi Kosyfaki, Konstantinos Lampropoulos, N. Mamoulis

引用次数: 0

Minimum Entropy Principle Guided Graph Neural Networks 最小熵原理引导图神经网络

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3570467

Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan.Z Sheng, Hao Peng, Ang Li, Shan Xue, Jianlin Su

{"title":"Minimum Entropy Principle Guided Graph Neural Networks","authors":"Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan.Z Sheng, Hao Peng, Ang Li, Shan Xue, Jianlin Su","doi":"10.1145/3539597.3570467","DOIUrl":"https://doi.org/10.1145/3539597.3570467","url":null,"abstract":"Graph neural networks (GNNs) are now the mainstream method for mining graph-structured data and learning low-dimensional node- and graph-level embeddings to serve downstream tasks. However, limited by the bottleneck of interpretability that deep neural networks present, existing GNNs have ignored the issue of estimating the appropriate number of dimensions for the embeddings. Hence, we propose a novel framework called Minimum Graph Entropy principle-guided Dimension Estimation, i.e. MGEDE, that learns the appropriate embedding dimensions for both node and graph representations. In terms of node-level estimation, a minimum entropy function that counts both structure and attribute entropy, appraises the appropriate number of dimensions. In terms of graph-level estimation, each graph is assigned a customized embedding dimension from a candidate set based on the number of dimensions estimated for the node-level embeddings. Comprehensive experiments with node and graph classification tasks and nine benchmark datasets verify the effectiveness and generalizability of MGEDE.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124392590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Developing and Evaluating Graph Counterfactual Explanation with GRETEL 用GRETEL发展和评价图反事实解释

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3573026

Mario Alfonso Prado-Romero, Bardh Prenkaj, G. Stilo

引用次数: 2

A Tutorial on Domain Generalization 关于领域泛化的教程

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI: 10.1145/3539597.3572722

Jindong Wang, Haoliang Li, Sinno Jialin Pan, Xingxu Xie

引用次数: 0