Maximilien Danisch

Finding Heaviest k-Subgraphs and Events in Social Media

In recent years, social media have become a useful tool to stay in contact with friends, to share thoughts but also to be informed about events. Users can follow news channels, but they can be the ones reporting updates, which distinguishes social media from traditional media. In this paper, we use a graph mining approach for finding events in a graph constructed starting from posts of users. We develop an exact algorithm for solving the heaviest k-subgraph problem which is an NP-hard problem. Our experimental analysis on large real-world graphs shows that our algorithm is able to compute the exact solutions for k up to 15 or more depending on the structure of the graph. We also develop an approximation version of our algorithm scaling to larger k. In comparison, for this setting, the classical heuristic based on weighted core decomposition only leads to sub-optimal solutions. Finally, we show that our algorithm can be used to find relevant events in Twitter. Indeed, as an event is usually described by a small number of words, our algorithm is a useful tool to detect them.

On the importance of considering social capitalism when measuring influence on Twitter

Influence on Twitter has drawn a lot of attention these past few years since this microblogging service is used to share, seek or debate about any kind of information. Several tools providing so-called influential scores have thus been proposed. However, the algorithms behind them are kept secret and it is not clear how they consider influence. Yet, many users rely on such tools to evaluate and even try to improve their influence in the Twitter network. In a recent work, it has been shown that automatic accounts can obtain high influential scores with no intuitive reason. Extending and completing this work, we show that such measures fail at distinguishing so-called social capitalists from real, truthful users. This enlights the fact that actual scores do not seem to consider the way followers and interactions are obtained on the network. To overcome this issue, we define a classifier that discriminates social capitalists from truthful users. To that aim, we crawled the Twitter network to gather examples of certified social capitalists and regular users and obtained features related to the profile and behavior of each user. We then use such a classifier to balance Klouts score to adjust influential scores. We also developed an application that allows using our classifier online. We believe our work should raise the question of the legitimacy of influence on Twitter, and lead to significant improvements in the way it is measured.

A reliable and evolutive web application to detect social capitalists

On Twitter, social capitalists use dedicated hashtags and mutual subscriptions to each other in order to gain followers and to be retweeted. Their methods are successful enough to make them appear as influent users. Indeed, applications dedicated to the influence measurement such as Klout and Kred give high scores to most of these users. Meanwhile, their high number of retweets and followers are not due to the relevance of the content they tweet, but to their social capitalism techniques. In order to be able to detect these users, we train a classifier using a dataset of social capitalists and regular users. We then implement this classifier in a web application that we call DDP. DDP allows users to test whether a Twitter account is a social capitalist or not and to visualize the data we use to make the prediction. DDP allows administrator to crawl data from a lot of users automatically. Furthermore, administrators can manually label Twitter accounts as social capitalists or regular users to add them into the dataset. Finally, administrators can train new classifiers in order to take into account the new Twitter accounts added to the dataset, and thus making evolve the classifier with these new recently collected data. The web application is thus a way to collect data, make evolve the knowledge about social capitalists and to keep detecting them efficiently.

Listing

Motivated by recent studies in the data mining community which require to efficiently list all k-cliques, we revisit the iconic algorithm of Chiba and Nishizeki and develop the most efficient parallel algorithm for such a problem. Our theoretical analysis provides the best asymptotic upper bound on the running time of our algorithm for the case when the input graph is sparse. Our experimental evaluation on large real-world graphs shows that our parallel algorithm is faster than state-of-the-art algorithms, while boasting an excellent degree of parallelism. In particular, we are able to list all k-cliques (for any k) in graphs containing up to tens of millions of edges as well as all

k

10

-cliques in Sparse Real-World Graphs

-cliques in graphs containing billions of edges, within a few minutes and a few hours respectively. Finally, we show how our algorithm can be employed as an effective subroutine for finding the k-clique core decomposition and an approximate k-clique densest subgraphs in very large real-world graphs.

Modeling cascade formation in Twitter amidst mentions and retweets

This paper presents an analytical framework for cascade formation considering both retweet and mentioning activities into account. We introduce two mention strategies (a) random mention and (b) smart mention to model the mention preferences of the users. The proposed framework

Multi-ego-centered communities in practice

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