Revealing intricate properties of communities in the bipartite structure of online social networks

Tackx, Raphal; Guillaume, Jean‐Loup; Tarissan, Fabien

doi:10.1109/rcis.2015.7128892

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…However, several extensions of the clustering coefficient have been proposed [6,21,32,35,44] to serve as proxy for this notion in bipartite graphs. These proxies have proven to be useful in many contexts, ranging from improving the modelling of the large-scale link structure of the Internet [39], to analysing online social networks [36], or detecting landmark decisions in judicial decision networks [38].…”

Section: Length Of Paths: Short Paths Between Vertices Representingmentioning

confidence: 99%

Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval

Lioma

Tarissan

Simonsen

et al. 2016

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval

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Document coherence describes how much sense text makes in terms of its logical organisation and discourse flow. Even though coherence is a relatively difficult notion to quantify precisely, it can be approximated automatically. This type of coherence modelling is not only interesting in itself, but also useful for a number of other text processing tasks, including Information Retrieval (IR), where adjusting the ranking of documents according to both their relevance and their coherence has been shown to increase retrieval effectiveness [34,37].The state of the art in unsupervised coherence modelling represents documents as bipartite graphs of sentences and discourse entities, and then projects these bipartite graphs into one-mode undirected graphs. However, one-mode projections may incur significant loss of the information present in the original bipartite structure. To address this we present three novel graph metrics that compute document coherence on the original bipartite graph of sentences and entities. Evaluation on standard settings shows that: (i) one of our coherence metrics beats the state of the art in terms of coherence accuracy; and (ii) all three of our coherence metrics improve retrieval effectiveness because, as closer analysis reveals, they capture aspects of document quality that go undetected by both keyword-based standard ranking and by spam filtering. This work contributes document coherence metrics that are theoretically principled, parameter-free, and useful to IR.

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Section: Length Of Paths: Short Paths Between Vertices Representingmentioning

confidence: 99%

Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval

Lioma

Tarissan

Simonsen

et al. 2016

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval

Self Cite

View full text Add to dashboard Cite

show abstract

“…The affiliation of users to groups is considered as rich information that can be used by network researchers, sociologists, application designers and others for data mining tasks. For example, authors in [2] propose new metrics, namely the dispersion and the monopoly coefficients to refining the study of bipartite structures, particularly, when there is a community neighborhood overlapping. These two metrics are used to capture the intricate patterns observed in real social networks.…”

Section: Introductionmentioning

confidence: 99%

Privacy preservation for social networks sequential publishing

2020

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The proliferation of social networks allowed creating a big quantity of data about users and their relationships. Such data contain much private information. Therefore, anonymization is required before publishing the data for data mining purposes (scientific research, marketing, decision support, etc). Most of the anonymization works in social networks focus on publishing one instance while not considering the need for anonymizing sequential releases. However, many cases show that sequential releases may infer private information even though individual instances are anonymized. This paper studies the privacy issues of sequential releases and proposes a privacy preserving solution for this case. The proposed solution ensures three privacy requirements (users' privacy, groups' privacy and edges' privacy), and it considers the case where many users and groups may share the same profiles. Some experiments over some complex queries show that the utility of the released data is better preserved than other solutions, with regard to the privacy of users, groups and edges.

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The role of bipartite structure in R&D collaboration networks

Filho

O’Neale

2020

Journal of Complex Networks

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A great number of real-world networks are, in fact, one-mode projections of bipartite networks comprised of two different types of nodes. In the case of interactions between institutions engaging in collaboration for technological innovation, the underlying network is bipartite with institutions (agents) linked to the patents they have filed (artefacts), while the projection is the co-patenting network. Since projected network properties are highly affected by the underlying bipartite structure a lack of understanding of the bipartite network has consequences for the information that might be drawn from the one-mode co-patenting network. Here, we create an empirical bipartite network using data from 2.7 million patents recorded by the European Patent Office. We project this network onto the agents (institutions) and look at properties of both the bipartite and projected networks that may play a role in knowledge sharing and collaboration. We compare these empirical properties to those of synthetic bipartite networks and their projections. We show that understanding the bipartite network topology is critical for understanding the potential flow of technological knowledge. Properties of the bipartite structure, such as degree distributions and small cycles, affect the topology of the one-mode projected network—specifically degree and clustering distributions, and degree assortativity. We propose new network-based metrics as a way to quantify how collaborative agents are in the collaboration network. We find that several large corporations are the most collaborative agents in the network; however, such organizations tend to have a low diversity of collaborators. In contrast, the most prolific institutions tend to collaborate relatively little but with a diverse set of collaborators. This indicates that they concentrate the knowledge of their core technical research while seeking specific complementary knowledge via collaboration with smaller institutions.

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Revealing intricate properties of communities in the bipartite structure of online social networks

Cited by 6 publications

References 26 publications

Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval

Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval

Privacy preservation for social networks sequential publishing

The role of bipartite structure in R&D collaboration networks

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