Flow Computation in Temporal Interaction Networks

Kosyfaki, Chrysanthi; Mamoulis, Nikos; Pitoura, Evaggelia; Tsaparas, Panayiotis

doi:10.1109/icde51399.2021.00063

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…Researchers have analyzed temporal motifs within and across a wide-range of networks [24], and have proposed sampling methods to estimate their counts [21]. Temporal motifs have also been extended to capture the flow of information among nodes in a motif [16]. Among different motifs, triangle counting has attracted significant interest [28].…”

Section: Related Workmentioning

confidence: 99%

“…this goal, existing works have focused on counting patterns in networks. These patterns include temporal motifs (small subgraph patterns) [14,17,21,24], frequent subgraphs in evolving networks [3,4,6], flow motifs [16], communication motifs [30], and coevolving relational motifs [5]. This line of research uses the frequency of patterns towards understanding the behavior of networks (e.g., some interaction patterns are more common in Q&A forums than in instant messengers [24]).…”

Section: Introductionmentioning

confidence: 99%

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Mining Persistent Activity in Continually Evolving Networks

Belth

Zheng

Koutra

2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Frequent pattern mining is a key area of study that gives insights into the structure and dynamics of evolving networks, such as social or road networks. However, not only does a network evolve, but often the way that it evolves, itself evolves. Thus, knowing, in addition to patterns' frequencies, for how long and how regularly they have occurred-i.e., their persistence-can add to our understanding of evolving networks. In this work, we propose the problem of mining activity that persists through time in continually evolving networks-i.e., activity that repeatedly and consistently occurs. We extend the notion of temporal motifs to capture activity among specific nodes, in what we call activity snippets, which are small sequences of edge-updates that reoccur. We propose axioms and properties that a measure of persistence should satisfy, and develop such a persistence measure. We also propose PENminer, an efficient framework for mining activity snippets' Persistence in Evolving Networks, and design both offline and streaming algorithms. We apply PENminer to numerous real, large-scale evolving networks and edge streams, and find activity that is surprisingly regular over a long period of time, but too infrequent to be discovered by aggregate count alone, and bursts of activity exposed by their lack of persistence. Our findings with PENminer include neighborhoods in NYC where taxi traffic persisted through Hurricane Sandy, the opening of new bike-stations, characteristics of social network users, and more. Moreover, we use PENminer towards identifying anomalies in multiple networks, outperforming baselines at identifying subtle anomalies by 9.8-48% in AUC.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining Persistent Activity in Continually Evolving Networks

Belth

Zheng

Koutra

2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

show abstract

“…Zhao et al [49] and Gurukar et al [9] studied the communication motifs, which are frequent subgraphs to characterize the patterns of information propagation in social networks. Kovanen et al [17] and Kosyfaki et al [16] defined the flow motifs to model flow transfer among a set of vertices within a time window in temporal networks. Although both definitions accounted for edge ordering, they were more restrictive than ours because the former assumed any two adjacent edges must occur within a fixed time span while the latter assumed edges in a motif must be consecutive events for a vertex [27].…”

Section: Related Workmentioning

confidence: 99%

Efficient Sampling Algorithms for Approximate Temporal Motif Counting (Extended Version)

Wang,

Jiang

et al. 2020

Preprint

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A great variety of complex systems ranging from user interactions in communication networks to transactions in financial markets can be modeled as temporal graphs, which consist of a set of vertices and a series of timestamped and directed edges. Temporal motifs in temporal graphs are generalized from subgraph patterns in static graphs which take into account edge orderings and durations in addition to structures. Counting the number of occurrences of temporal motifs is a fundamental problem for temporal network analysis. However, existing methods either cannot support temporal motifs or suffer from performance issues. In this paper, we focus on approximate temporal motif counting via random sampling. We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif. Furthermore, we devise an improved EWS algorithm that hybridizes edge sampling with wedge sampling for counting temporal motifs with 3 vertices and 3 edges. We provide comprehensive analyses of the theoretical bounds and complexities of our proposed algorithms. Finally, we conduct extensive experiments on several real-world datasets, and the results show that our ES and EWS algorithms have higher efficiency, better accuracy, and greater scalability than the state-of-the-art sampling method for temporal motif counting.

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“…Kosyfaki et al [18] defined and studied the enumeration of flow motifs in interaction networks, considering both the time and the flow on the interactions. Such motifs come with two constraints: the maximum possible duration a motif instance and the minimum possible flow of the motif.…”

Section: Network Patternsmentioning

confidence: 99%

“…Such motifs come with two constraints: the maximum possible duration a motif instance and the minimum possible flow of the motif. Although we also study the enumeration of flow patterns in Section 5, (i) our flow computation model is very different compared to the one in [18], as we consider maximum flow computation and also allow time-interleaving sequences of interactions, (ii) we study patterns that are not limited to simple paths, (iii) we propose precomputation approaches for pattern enumeration.…”

Section: Network Patternsmentioning

confidence: 99%

Flow Computation in Temporal Interaction Networks

Kosyfaki¹,

Mamoulis²,

Pitoura³

et al. 2020

Preprint

Self Cite

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Temporal interaction networks capture the history of activities between entities along a timeline. At each interaction, some quantity of data (money, information, kbytes, etc.) flows from one vertex of the network to another. Flow-based analysis can reveal important information. For instance, financial intelligent units (FIUs) are interested in finding subgraphs in transactions networks with significant flow of money transfers. In this paper, we introduce the flow computation problem in an interaction network or a subgraph thereof. We propose and study two models of flow computation, one based on a greedy flow transfer assumption and one that finds the maximum possible flow. We show that the greedy flow computation problem can be easily solved by a single scan of the interactions in time order. For the harder maximum flow problem, we propose graph precomputation and simplification approaches that can greatly reduce its complexity in practice. As an application of flow computation, we formulate and solve the problem of flow pattern search, where, given a graph pattern, the objective is to find its instances and their flows in a large interaction network. We evaluate our algorithms using real datasets. The results show that the techniques proposed in this paper can greatly reduce the cost of flow computation and pattern enumeration.

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Flow Computation in Temporal Interaction Networks

Cited by 5 publications

References 17 publications

Mining Persistent Activity in Continually Evolving Networks

Mining Persistent Activity in Continually Evolving Networks

Efficient Sampling Algorithms for Approximate Temporal Motif Counting (Extended Version)

Flow Computation in Temporal Interaction Networks

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