Network embedding, that aims to learn low-dimensional vector representation of nodes such that the network structure is preserved, has gained significant research attention in recent years. However, most state-of-the-art network embedding methods are computationally expensive and hence unsuitable for representing nodes in billion-scale networks. In this paper, we present LouvainNE, a hierarchical clustering approach to network embedding. Precisely, we employ Louvain, an extremely fast and accurate community detection method, to build a hierarchy of successively smaller subgraphs. We obtain representations of individual nodes in the original graph at different levels of the hierarchy, then we aggregate these representations to learn the final embedding vectors. Our theoretical analysis shows that our proposed algorithm has quasi-linear run-time and memory complexity. Our extensive experimental evaluation, carried out on multiple real-world networks of different scales, demonstrates both (i) the scalability of our proposed approach that can handle graphs containing tens of billions of edges, as well as (ii) its effectiveness in performing downstream network mining tasks such as network reconstruction and node classification.

While microblogging-based Online Social Networks have become an attractive data source in emergency situations, overcoming information overload is still not trivial. We propose a framework which integrates natural language processing and clustering techniques in order to produce a ranking of relevant tweets based on their informativeness. Experiments on four Twitter collections in two languages (English and French) proved the significance of our approach.

Community detection in single layer, isolated networks has been extensively studied in the past decade. However, many real-world systems can be naturally conceptualized as multilayer networks which embed multiple types of nodes and relations. In this paper, we propose algorithm for detecting communities in multilayer networks. The crux of the algorithm is based on the multilayer modularity index Q_M, developed in this paper. The proposed algorithm is parameter-free, scalable and adaptable to complex network structures. More importantly, it can simultaneously detect communities consisting of only single type, as well as multiple types of nodes (and edges). We develop a methodology to create synthetic networks with benchmark multilayer communities. We evaluate the performance of the proposed community detection algorithm both in the controlled environment (with synthetic benchmark communities) and on the empirical datasets (Yelp and Meetup datasets); in both cases, the proposed algorithm outperforms the competing state-of-the-art algorithms.

In this paper, we investigate the role of mentions on tweet propagation. We propose a novel tweet propagation model SIR_MF based on a multiplex network framework, that allows to analyze the effects of mentioning on final retweet count. The basic bricks of this model are supported by a comprehensive study of multiple real datasets and simulations of the model show a nice agreement with the empirically observed tweet popularity. Studies and experiments also reveal that follower count, retweet rate & profile similarity are important factors in gaining tweet popularity and allow to better understand the impact of the mention strategies on the retweet count. Interestingly, we analytically identify a critical retweet rate regulating the role of mention on the tweet popularity. Finally, our data driven simulation demonstrates that the proposed mention recommendation heuristic "Easy-Mention" outperforms the benchmark "Whom-To-Mention" algorithm.

In large-scale online complex networks (Wikipedia, Facebook, Twitter, etc.) finding nodes related to a specific topic is a strategic research subject. This article focuses on two central notions in this context: communities (groups of highly connected nodes) and proximity measures (indicating whether nodes are topologically close). We propose a parameterized proximity measure which, given a set of nodes belonging to a community, learns the optimal parameters and identifies the other nodes of this community, called multi-ego-centered community as it is centered on a set of nodes. We validate our results on a large dataset of categorized Wikipedia pages and on benchmarks, we also show that our approach performs better than existing ones. Our main contributions are (i) a new ergonomic parametrized proximity measure, (ii) the automatic tuning of the proximity's parameters and (iii) the unsupervised detection of community boundaries.

Diffusion phenomena occur in many kinds of real-world complex networks, e.g., biological, information or social networks. Because of this diversity, several types of diffusion models have been proposed in the literature: epidemiological models, threshold models, innovation adoption models, among others. Many studies aim at investigating diffusion as an evolving phenomenon but mostly occurring on static networks, and much remains to be done to understand diffusion on evolving networks. In order to study the impact of graph dynamics on diffusion, we propose in this paper an innovative approach based on a notion of intrinsic time, where the time unit corresponds to the appearance of a new link in the graph. This original notion of time allows us to isolate somehow the diffusion phenomenon from the evolution of the network. The objective is to compare the diffusion features observed with this intrinsic time concept from those obtained with traditional (extrinsic) time, based on seconds. The comparison of these time concepts is easily understandable yet completely new in the study of diffusion phenomena. We experiment our approach on synthetic graphs, as well as on a dataset extracted from the Github sofware sharing platform.

Communities are a powerful tool to describe the structure of complex networks. Algorithms aiming at maximizing a quality function called modularity have been shown to effectively compute the community structure. However, some problems remain: in particular, it is possible to find high modularity partitions in graph without any community structure, in particular random graphs. In this paper, we study the notion of consensual communities and show that they do not exist in random graphs. For that, we exhibit a phase transition based on the strength of consensus: below a given threshold, all the nodes belongs to the same consensual community; above this threshold, each node is in its own consensual community.