Applied Network Science最新文献_第3页

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection 探索时间群落进化:动态群落检测的算法方法和并行优化

Applied Network Science Pub Date : 2023-09-18 DOI: 10.1007/s41109-023-00592-1

Naw Safrin Sattar, Aydin Buluc, Khaled Z. Ibrahim, Shaikh Arifuzzaman

{"title":"Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection","authors":"Naw Safrin Sattar, Aydin Buluc, Khaled Z. Ibrahim, Shaikh Arifuzzaman","doi":"10.1007/s41109-023-00592-1","DOIUrl":"https://doi.org/10.1007/s41109-023-00592-1","url":null,"abstract":"Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar , which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135153405","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

Fingerprinting Bitcoin entities using money flow representation learning 使用现金流表征学习识别比特币实体

Applied Network Science Pub Date : 2023-09-15 DOI: 10.1007/s41109-023-00591-2

Natkamon Tovanich, Rémy Cazabet

{"title":"Fingerprinting Bitcoin entities using money flow representation learning","authors":"Natkamon Tovanich, Rémy Cazabet","doi":"10.1007/s41109-023-00591-2","DOIUrl":"https://doi.org/10.1007/s41109-023-00591-2","url":null,"abstract":"Abstract Deanonymization is one of the major research challenges in the Bitcoin blockchain, as entities are pseudonymous and cannot be identified from the on-chain data. Various approaches exist to identify multiple addresses of the same entity, i.e., address clustering. But it is known that these approaches tend to find several clusters for the same actor. In this work, we propose to assign a fingerprint to entities based on the dynamic graph of the taint flow of money originating from them, with the idea that we could identify multiple clusters of addresses belonging to the same entity as having similar fingerprints. We experiment with different configurations to generate substructure patterns from taint flows before embedding them using representation learning models. To evaluate our method, we train classification models to identify entities from their fingerprints. Experiments show that our approach can accurately classify entities on three datasets. We compare different fingerprint strategies and show that including the temporality of transactions improves classification accuracy and that following the flow for too long impairs performance. Our work demonstrates that out-flow fingerprinting is a valid approach for recognizing multiple clusters of the same entity.","PeriodicalId":37010,"journal":{"name":"Applied Network Science","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135394663","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

Spotting the stock and crypto markets’ rings of fire: measuring change proximities among spillover dependencies within inter and intra-market asset classes 发现股票和加密市场的火环:衡量市场间和市场内资产类别的溢出依赖关系的变化接近度

IF 2.2

Applied Network Science Pub Date : 2023-09-08 DOI: 10.1007/s41109-023-00589-w

Hendra Setiawan, Moinak Bhaduri

引用次数: 1

Normalized closeness centrality of urban networks: impact of the location of the catchment area and evaluation based on an idealized network 城市网络的归一化封闭中心性：集水区位置的影响和基于理想化网络的评估

IF 2.2

Applied Network Science Pub Date : 2023-09-06 DOI: 10.1007/s41109-023-00585-0

Hsiao-Hui Chen, Udo Dietrich

引用次数: 0

Centrality in the macroeconomic multi-network explains the spatiotemporal distribution of country per-capita income 宏观经济多网络中的中心性解释了国家人均收入的时空分布