Discovering Patterns of Interest in IP Traffic Using Cliques in Bipartite Link Streams

Viard, Tiphaine; Fournier-S’niehotta, Raphaël; Magnien, Clémence; Latapy, Matthieu

doi:10.1007/978-3-319-73198-8_20

Cited by 13 publications

(17 citation statements)

References 14 publications

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“…A maximum ∆-clique enumeration is designed and evaluated using real-world data. The aforementioned framework is exploited in [97] for discovering patterns of interest in IP traffic using cliques in bipartite link streams.…”

Section: E Special Algorithms For the Clique Problemmentioning

confidence: 99%

A Tutorial on Clique Problems in Communications and Signal Processing

et al. 2020

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Since its first use by Euler on the seven bridges of Königsberg problem, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems notably in communications and signal processing. The study of the structure of integer programs reveals equivalence with graph theory problems, which makes a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of problems that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial formulates and suggests various optimal and heuristic solutions for the clique problem and its variants. The tutorial, further, illustrates the use of the clique formulation and its variants through numerous contemporary examples in communications and signal processing, mainly in maximum access problem for non-orthogonal multiple access networks, throughput maximization using index coding and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. The tutorial finally sheds light on the recent advances of such applications and discusses future research directions on the topics.

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Section: E Special Algorithms For the Clique Problemmentioning

confidence: 99%

A Tutorial on Clique Problems in Communications and Signal Processing

et al. 2020

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“…Transitivity is usually extended to bipartite graphs [43,27,38] by considering the set N of quadruplets of nodes (a, b, c, d) such that ab, bc and cd are in E and the set ⊲⊳ of such quadruplets with in addition ad in E. Then, the transitivity is tr(G) = |⊲⊳| |N| . Like above, [38] also propose to consider the fraction of all quintuplets (a, b, c, d, e) of distinct nodes with ab, bc, cd, de in E such that there exists an other node f with af and ef in E. Bipartite stream graphs naturally model many situations, like for instance presence of people in rooms or other kinds of locations, purchases of products by clients, access to on-line services, bus presence at stations [15], traffic between a set of computers and the rest of the internet [48], or contribution of people to projects, such as software.…”

Section: Bipartite Stream Graphsmentioning

confidence: 99%

Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks

Latapy

Magnien²,

Viard³

2019

Computational Social Sciences

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We recently introduced a formalism for the modeling of temporal networks, that we call stream graphs. It emphasizes the streaming nature of data and allows rigorous definitions of many important concepts generalizing classical graphs. This includes in particular size, density, clique, neighborhood, degree, clustering coefficient, and transitivity. In this contribution, we show that, like graphs, stream graphs may be extended to cope with bipartite structures, with node and link weights, or with link directions. We review the main bipartite, weighted or directed graph concepts proposed in the literature, we generalize them to the cases of bipartite, weighted, or directed stream graphs, and we show that obtained concepts are consistent with graph and stream graph ones. This provides a formal ground for an accurate modeling of the many temporal networks that have one or several of these features.

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“…Plus, some cliques (like stars) do not capture relevant information for recommendation. We then use the methodology described in [25] to sample maximal balanced bipartite cliques, i.e. cliques involving approximately the same number of users and items.…”

Section: Graph and Link Stream Featuresmentioning

confidence: 99%

Augmenting content-based rating prediction with link stream features

Viard

Fournier-S’niehotta

2019

Computer Networks

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While graph-based collaborative filtering recommender systems have been introduced several years ago, there are still several shortcomings to deal with, the temporal information being one of the most important. The new link stream paradigm is aiming at extending graphs for correctly modelling the graph dynamics, without losing crucial information. We investigate the impact of such link stream features for recommender systems. We design link stream features, that capture the intrinsic structure and dynamics of the data. We show that such features encode a fine-grained and subtle description of the underlying system. We focused on a traditional recommender system context, the rating prediction on the MovieLens20M movie dataset and the Goodreads book dataset. We input link stream features along with some content-based ones into a gradient boosting machine (XGBoost) and show that it outperforms significantly a sole content-based solution. These encouraging results call for further exploration of this original modelling and its integration to complete state-of-the-art recommender systems algorithms. Link streams and graphs, as natural visualizations of recommender systems, may offer more interpretability in a time when algorithm transparency is an increasingly important topic of discussion. We also hope that these results will sparkle interesting discussions in the community about the connections between link streams and traditional methods (matrix factorization, deep learning).

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Discovering Patterns of Interest in IP Traffic Using Cliques in Bipartite Link Streams

Cited by 13 publications

References 14 publications

A Tutorial on Clique Problems in Communications and Signal Processing

A Tutorial on Clique Problems in Communications and Signal Processing

Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks

Augmenting content-based rating prediction with link stream features

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