Discovering Colored Network Motifs

Ribeiro, Pedro; Silva, Fernando M. A.

doi:10.1007/978-3-319-05401-8_11

Cited by 31 publications

(22 citation statements)

References 24 publications

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“…In temporal networks, temporal motifs are topologically equivalent patterns that additionally include the same order of events, confined to occur within a specified period of time (Kovanen et al, 2011;Paranjape et al, 2017). If we assign colors to nodes (Kovanen et al, 2013;Ribeiro & Silva, 2014), then we can represent social contagion as motifs in which a node changes color after interacting with a colored node. Although the event sequences we study here represent elementary subgraph structures, temporal motif analysis allows to extend our research to explore longer and more complex chains of influence.…”

Section: (B) (A)mentioning

confidence: 99%

Cheating in online gaming spreads through observation and victimization

Kim

Tsvetkova

2021

Net Sci

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Antisocial behavior can be contagious, spreading from individual to individual and rippling through social networks. Moreover, it can spread not only through third-party influence from observation, just like innovations or individual behavior do, but also through direct experience, via “pay-it-forward” retaliation. Here, we distinguish between the effects of observation and victimization for the contagion of antisocial behavior by analyzing large-scale digital trace data. We study the spread of cheating in more than a million matches of an online multiplayer first-person shooter game, in which up to 100 players compete individually or in teams against strangers. We identify event sequences in which a player who observes or is killed by a certain number of cheaters starts cheating and evaluate the extent to which these sequences would appear if we preserve the team and interaction structure but assume alternative gameplay scenarios. The results reveal that social contagion is only likely to exist for those who both observe and experience cheating, suggesting that third-party influence and “pay-it-forward” reciprocity interact positively. In addition, the effect is present only for those who both observe and experience more than once, suggesting that cheating is more likely to spread after repeated or multi-source exposure. Approaching online games as models of social systems, we use the findings to discuss strategies for targeted interventions to stem the spread of cheating and antisocial behavior more generally in online communities, schools, organizations, and sports.

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Section: (B) (A)mentioning

confidence: 99%

Cheating in online gaming spreads through observation and victimization

Kim

Tsvetkova

2021

Net Sci

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“…There is rich history of work on triangle counting in static graphs. Various algorithm for triangle and motif counting in attributed graphs have also been proposed [13,30,37,41,42,56]. Here we only focus on temporal networks and refer the reader to [2] and the tutorial [44] for a more detailed list of related work.…”

Section: Related Workmentioning

confidence: 99%

Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering

Pashanasangi

Seshadhri

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Triangle counting is a fundamental technique in network analysis, that has received much attention in various input models. The vast majority of triangle counting algorithms are targeted to static graphs. Yet, many real-world graphs are directed and temporal, where edges come with timestamps. Temporal triangles yield much more information, since they account for both the graph topology and the timestamps.Temporal triangle counting has seen a few recent results, but there are varying definitions of temporal triangles. In all cases, temporal triangle patterns enforce constraints on the time interval between edges (in the triangle). We define a general notion ( 1,3 , 1,2 , 2,3 )-temporal triangles that allows for separate time constraints for all pairs of edges.Our main result is a new algorithm, DOTTT (Degeneracy Oriented Temporal Triangle Totaler), that exactly counts all directed variants of ( 1,3 , 1,2 , 2,3 )-temporal triangles. Using the classic idea of degeneracy ordering with careful combinatorial arguments, we can prove that DOTTT runs in ( log ) time, where is the number of (temporal) edges and is the graph degeneracy (max core number). Up to log factors, this matches the running time of the best static triangle counters. Moreover, this running time is better than existing.DOTTT has excellent practical behavior and runs twice as fast as existing state-of-the-art temporal triangle counters (and is also more general). For example, DOTTT computes all types of temporal queries in Bitcoin temporal network with half a billion edges in less than an hour on a commodity machine. CCS CONCEPTS• Theory of computation → Graph algorithms analysis; • Information systems → Data mining; Social networks; • Mathematics of computing → Graph algorithms.

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“…For more specific details on how a g-trie is created and used for subgraph enumeration the reader is referred to [32]. Additionally, g-tries have already been extended to also support weighted [34] and colored [35] networks. A tool for motif discovery making use of the g-trie datastructure has already been released 1 .…”

Section: Subgraph Census With a G-triementioning

confidence: 99%

Extending the Applicability of Graphlets to Directed Networks

Aparício

Ribeiro

Silva

2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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With recent advances in high-throughput cell biology, the amount of cellular biological data has grown drastically. Such data is often modeled as graphs (also called networks) and studying them can lead to new insights into molecule-level organization. A possible way to understand their structure is by analyzing the smaller components that constitute them, namely network motifs and graphlets. Graphlets are particularly well suited to compare networks and to assess their level of similarity due to the rich topological information that they offer but are almost always used as small undirected graphs of up to five nodes, thus limiting their applicability in directed networks. However, a large set of interesting biological networks such as metabolic, cell signaling, or transcriptional regulatory networks are intrinsically directional, and using metrics that ignore edge direction may gravely hinder information extraction. Our main purpose in this work is to extend the applicability of graphlets to directed networks by considering their edge direction, thus providing a powerful basis for the analysis of directed biological networks. We tested our approach on two network sets, one composed of synthetic graphs and another of real directed biological networks, and verified that they were more accurately grouped using directed graphlets than undirected graphlets. It is also evident that directed graphlets offer substantially more topological information than simple graph metrics such as degree distribution or reciprocity. However, enumerating graphlets in large networks is a computationally demanding task. Our implementation addresses this concern by using a state-of-the-art data structure, the g-trie, which is able to greatly reduce the necessary computation. We compared our tool to other state-of-the art methods and verified that it is the fastest general tool for graphlet counting.

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Discovering Colored Network Motifs

Cited by 31 publications

References 24 publications

Cheating in online gaming spreads through observation and victimization

Cheating in online gaming spreads through observation and victimization

Faster and Generalized Temporal Triangle Counting, via Degeneracy Ordering

Extending the Applicability of Graphlets to Directed Networks

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