2023 IEEE 39th International Conference on Data Engineering (ICDE)最新文献_第10页

Optimizing Graph Partition by Optimal Vertex-Cut: A Holistic Approach 用最优顶点切割优化图分割:一种整体方法

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00083

Wenwen Qu, Weixi Zhang, Ji Cheng, Chaorui Zhang, Wei Han, Bo Bai, Chen Jason Zhang, Liang He, Xiaoling Wang

{"title":"Optimizing Graph Partition by Optimal Vertex-Cut: A Holistic Approach","authors":"Wenwen Qu, Weixi Zhang, Ji Cheng, Chaorui Zhang, Wei Han, Bo Bai, Chen Jason Zhang, Liang He, Xiaoling Wang","doi":"10.1109/ICDE55515.2023.00083","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00083","url":null,"abstract":"Graph partitioning is crucial in distributed graph-parallel computing systems, and it is challenging for graph partitioning to optimize the communication cost and load balancing together. Existing state-of-the-art works, such as Powerlyra and TopoX, optimize the load balancing by randomly distributing the edges of high-degree vertices, which inevitably brings a high communication cost that is unbounded. This paper proposes a graph partition model that can minimize communication cost while maximizing load balancing. More specifically, we model the graph partition as the combinatorial design problem. Our proposed model can provide high-quality partition that guarantees that the computing load can be evenly distributed to each worker and minimizes the communication cost with a near-optimal theoretical boundary.Based on the proposed model, we extend the hybrid-cut partitioning algorithm for the power-law graph and propose HCPD, a hybrid-cut partitioning algorithm based on combinatorial design. HCPD uses the proposed model to optimize the load balancing and communication cost simultaneously for high-degree vertices, and assigns the high-degree vertices and their low-degree neighbors to the same workers by label propagation to reduce the overall communication cost. In this way, we partition the low-degree and high-degree vertices holistically and further improve the partition quality, unlike Powerlyra and TopoX, which deal with the two parts independently. Our experiments show that HCPD outperforms Powerlyra on PageRank task by up to 2× faster on real-world power-law graphs with billions of edges.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115336635","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: 0

Consensus One-step Multi-view Subspace Clustering (Extended abstract) 共识一步多视图子空间聚类(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00307

Pei Zhang, Xinwang Liu, Jian Xiong, Sihang Zhou, Wentao Zhao, En Zhu, Zhiping Cai

引用次数: 0

Distributed Near-Maximum Independent Set Maintenance over Large-scale Dynamic Graphs 大规模动态图上的分布近极大独立集维护

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00195

Xubo Wang, Dong Wen, Wenjie Zhang, Y. Zhang, Lu Qin

{"title":"Distributed Near-Maximum Independent Set Maintenance over Large-scale Dynamic Graphs","authors":"Xubo Wang, Dong Wen, Wenjie Zhang, Y. Zhang, Lu Qin","doi":"10.1109/ICDE55515.2023.00195","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00195","url":null,"abstract":"Computing the maximum independent set (MIS) in a graph is a fundamental NP-hard problem, which is widely adopted in many real-world applications. Extensive works have been done on computing an approximate MIS. While the highly dynamic property of real-world graphs calls for efficient MIS maintenance solutions, existing works for dynamic MIS computation in the literature mainly focus on the single-machine scenario. The assumption that a single machine can access the whole graph makes them difficult to be straightforwardly applied for large-scale graphs in distributed environment. Motivated by this, in this paper, we study the problem of maintaining approximate MIS over large-scale dynamic graphs in distributed environments. We propose a new vertex centric algorithm OIMIS. Compared with existing solutions, OIMIS avoids the strong order dependency in distributed computation, which makes it easy to handle dynamic graph updates. OIMIS computes and maintains MIS with high effectiveness and efficiency. In terms of high effectiveness, OIMIS maintains consistent MIS results with the state-of-the-art distributed algorithm to compute MIS in static graphs. In terms of high efficiency, each vertex in OIMIS only updates MIS status according to its neighbor attributes. Novel optimization techniques are also designed to reduce communication and computation cost. We conduct extensive experiments to prove the effectiveness and efficiency of our distributed algorithms.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116242616","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: 0

ALT: An Automatic System for Long Tail Scenario Modeling ALT:一个长尾情景建模的自动系统

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00231

Ya-Lin Zhang, Jun Zhou, Yankun Ren, Yue Zhang, Xinxing Yang, Meng Li, Qitao Shi, Longfei Li

引用次数: 0

Data Dependencies Extended for Variety and Veracity: A Family Tree (Extended abstract) 为了多样性和准确性而扩展的数据依赖:一个家谱(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00336

Shaoxu Song, Fei Gao, Ruihong Huang, Chaokun Wang

引用次数: 0

A Hierarchical Approach to Anomalous Subgroup Discovery 异常子群发现的分层方法

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00203

Eliana Pastor

{"title":"A Hierarchical Approach to Anomalous Subgroup Discovery","authors":"Eliana Pastor","doi":"10.1109/ICDE55515.2023.00203","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00203","url":null,"abstract":"Understanding peculiar and anomalous behavior of machine learning models for specific data subgroups is a fundamental building block of model performance and fairness evaluation. The analysis of these data subgroups can provide useful insights into model inner working and highlight its potentially discriminatory behavior. Current approaches to subgroup exploration ignore the presence of hierarchies in the data, and can only be applied to discretized attributes. The discretization process required for continuous attributes may significantly affect the identification of relevant subgroups.We propose a hierarchical subgroup exploration technique to identify anomalous subgroup behavior at multiple granularity levels, along with a technique for the hierarchical discretization of data attributes. The hierarchical discretization produces, for each continuous attribute, a hierarchy of intervals. The subsequent hierarchical exploration can exploit data hierarchies, selecting for each attribute the optimal granularity to identify subgroups that are both anomalous, and with enough elements to be statistically and practically significant. Compared to non- hierarchical approaches, we show that our hierarchical approach is more powerful in identifying anomalous subgroups and more stable with respect to discretization and exploration parameters.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125327243","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: 0

SketchConf: A Framework for Automatic Sketch Configuration SketchConf:一个自动配置草图的框架

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00157

Ruijie Miao, Fenghao Dong, Yikai Zhao, Yiming Zhao, Yuhan Wu, Kaicheng Yang, Tong Yang, Bin Cui

引用次数: 0

Modeling Product’s Visual and Functional Characteristics for Recommender Systems (Extended Abstract) 面向推荐系统的产品视觉和功能特征建模(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00345

Bin Wu, Xiangnan He, Yu Chen, Liqiang Nie, Kai Zheng, Yangdong Ye

引用次数: 1

A Deep Neural Network for Crossing-City POI Recommendations : (Extended Abstract) 跨城市POI推荐的深度神经网络(扩展摘要)

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00352

Dichao Li, Zhiguo Gong

引用次数: 0

COCLEP: Contrastive Learning-based Semi-Supervised Community Search 基于对比学习的半监督社区搜索

2023 IEEE 39th International Conference on Data Engineering (ICDE) Pub Date : 2023-04-01 DOI: 10.1109/ICDE55515.2023.00191

Ling Li, Siqiang Luo, Yuhai Zhao, Caihua Shan, Zhengkui Wang, Lu Qin

{"title":"COCLEP: Contrastive Learning-based Semi-Supervised Community Search","authors":"Ling Li, Siqiang Luo, Yuhai Zhao, Caihua Shan, Zhengkui Wang, Lu Qin","doi":"10.1109/ICDE55515.2023.00191","DOIUrl":"https://doi.org/10.1109/ICDE55515.2023.00191","url":null,"abstract":"Community search is a fundamental graph processing task that aims to find a community containing the given query node. Recent studies show that machine learning (ML)-based community search can return higher-quality communities than the classic methods such as k-core and k-truss. However, the state-of-the-art ML-based models require a large number of labeled data (i.e., nodes in ground-truth communities) for training that are difficult to obtain in real applications, and incur unaffordable memory costs or query time for large datasets. To address these issues, in this paper, we present the community search based on contrastive learning with partition, namely COCLEP, which only requires a few labels and is both memory and query efficient. In particular, given a small collection of query nodes and a few (e.g., three) corresponding ground-truth community nodes for each query, COCLEP learns a query-dependent model through the proposed graph neural network and the designed label-aware contrastive learner. The former perceives query node information, low-order neighborhood information, and high-order hypergraph structure information, the latter contrasts low-order intra-view, high-order intra-view, and low-high-order inter-view representations of the nodes. Further, we theoretically prove that COCLEP can be scalable to large datasets with the min-cut over the graph. To the best of our knowledge, this is the first attempt to adopt contrastive learning for community search task that is nontrivial. Extensive experiments on real-world datasets show that COCLEP simultaneously achieves better community effectiveness and comparably high query efficiency while using fewer labels compared with the-state-of-the-art approaches and is scalable for large datasets.","PeriodicalId":434744,"journal":{"name":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129867691","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