Cascade-based community detection

Barbieri, Nicola; Bonchi, Francesco; Manco, Giuseppe

doi:10.1145/2433396.2433403

Cited by 97 publications

(73 citation statements)

References 57 publications

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“…Therefore, in order to detect metadata groups, non-topological inputs might be necessary. In the most recent literature on community detection several such approaches have been proposed, mostly by computer scientists [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. We stress, however, that structural communities are very important for the function of a network, as they can significantly affect the dynamics of processes taking place on the network, such as diffusion, synchronization, opinion formation, etc.…”

Section: Discussionmentioning

confidence: 99%

Community detection in networks: Structural communities versus ground truth

2014

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Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (non-topological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.

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Section: Discussionmentioning

confidence: 99%

Community detection in networks: Structural communities versus ground truth

2014

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“…Models devoted to reproducing the growth and evolution of network topology have traditionally focused on defining basic mechanisms driving link creation [62,46,9]. From the first model proposed in 1959 by Erdös and Rényi [22], many others have been introduced capturing different properties observed in real networks, such as the small-world phenomenon [63,39,34,54], large clustering coefficient [63,39,34,54], temporal dynamics [51,53], information propagation [8], and heterogeneous distributions in connectivity patterns [7,33,37,35,20,23]. In particular, this latter property was first described by the preferential attachment [7] and copy models [37].…”

Section: Introductionmentioning

confidence: 99%

The role of information diffusion in the evolution of social networks

Weng

Ratkiewicz

Perra

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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140

107

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Every day millions of users are connected through online social networks, generating a rich trove of data that allows us to study the mechanisms behind human interactions. Triadic closure has been treated as the major mechanism for creating social links: if Alice follows Bob and Bob follows Charlie, Alice will follow Charlie. Here we present an analysis of longitudinal micro-blogging data, revealing a more nuanced view of the strategies employed by users when expanding their social circles. While the network structure affects the spread of information among users, the network is in turn shaped by this communication activity. This suggests a link creation mechanism whereby Alice is more likely to follow Charlie after seeing many messages by Charlie. We characterize users with a set of parameters associated with different link creation strategies, estimated by a Maximum-Likelihood approach. Triadic closure does have a strong effect on link formation, but shortcuts based on traffic are another key factor in interpreting network evolution. However, individual strategies for following other users are highly heterogeneous. Link creation behaviors can be summarized by classifying users in different categories with distinct structural and behavioral characteristics. Users who are popular, active, and influential tend to create traffic-based shortcuts, making the information diffusion process more efficient in the network. Categories and Subject Descriptors KeywordsLink creation, traffic, network evolution, information diffusion, shortcut, user behavior, social media, network structure Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

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“…Work on this topic encompasses the shrinking diameter and densification [25]; the power law for the mail response times of Einstein and Darwin, [30]; analysis of blog dynamics [16,26], and discovery of core-periphery patterns in blogs and news articles [15]; viral marketing [23,21]; meme tracking [24]; reciprocity analysis [14,6]; analysis of the role of weak and strong ties in information diffusion in mobile networks [31]; identification of important influencers [36]; prediction of service adoption in mobile communication networks [37]; information or cascade diffusion in social networks [9,4,8,38]; linguistic change in online forums, and predicting the user's lifespan based on her linguistic patterns [11]; peer and authority pressure in information propagation [7].…”

Section: Related Workmentioning

confidence: 99%

“…Social influence has been a topic of interest in the research community [38,30,16,23,21,9,4,8,11,27] because of the rise of various on-line social media and social networks. In this work, by social influence we refer to the fact that "individuals adopt a new action because of others".…”

Section: Introductionmentioning

confidence: 99%

Influence Propagation: Patterns, Model and a Case Study

Lin

Raza

Lee

et al. 2014

Advances in Knowledge Discovery and Data Mining

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Abstract. When a free, catchy application shows up, how quickly will people notify their friends about it? Will the enthusiasm drop exponentially with time, or oscillate? What other patterns emerge? Here we answer these questions using data from the Polly telephone-based application, a large influence network of 72,000 people, with about 173,000 interactions, spanning 500MB of log data and 200 GB of audio data. We report surprising patterns, the most striking of which are: (a) the FIZZLE pattern, i.e., excitement about Polly shows a power-law decay over time with exponent of -1.2; (b) the RENDEZVOUS pattern, that obeys a power law (we explain RENDEZVOUS in the text); (c) the DISPERSION pattern, we find that the more a person uses Polly, the fewer friends he will use it with, but in a reciprocal fashion. Finally, we also propose a generator of influence networks, which generate networks that mimic our discovered patterns.

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Cascade-based community detection

Cited by 97 publications

References 57 publications

Community detection in networks: Structural communities versus ground truth

Community detection in networks: Structural communities versus ground truth

The role of information diffusion in the evolution of social networks

Influence Propagation: Patterns, Model and a Case Study

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