Mining Temporal Networks
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A4 Artikkeli konferenssijulkaisussa
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2019
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en
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2
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KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3225-3226
Abstract
Networks (or graphs) are used to represent and analyze large datasets of objects and their relations. Naturally, real-world networks have a temporal component: for instance, interactions between objects have a timestamp and a duration. In this tutorial we present models and algorithms for mining temporal networks, i.e., network data with temporal information. We overview different models used to represent temporal networks. We highlight the main differences between static and temporal networks, and discuss the challenges arising from introducing the temporal dimension in the network representation. We present recent papers addressing the most well-studied problems in the setting of temporal networks, including computation of centrality measures, motif detection and counting, community detection and monitoring, event and anomaly detection, analysis of epidemic processes and influence spreading, network summarization, and structure prediction.Description
| openaire: EC/H2020/654024/EU//SoBigData
Keywords
data mining, graph mining, temporal networks
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Citation
Rozenshtein, P & Gionis, A 2019, Mining Temporal Networks . in KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, pp. 3225-3226, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Alaska, United States, 04/08/2019 . https://doi.org/10.1145/3292500.3332295