Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm

Parés, Ferran; Garcia-Gasulla, Darío; Vilalta, Armand; Moreno, J. L.; Ayguadé, Eduard; Labarta, Jesús; Cortés, Ulises; Suzumura, Toyotaro

doi:10.48550/arxiv.1703.09307

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…We explore the graph-representation by running a community detection algorithm on top of it. Particularly, we use the Fluid Communities (FluidC) algorithm [13]. We chose this algorithm because its based on the efficient label propagation methodology while outperforming the traditional LPA algorithm, because it allows us to specify the number of clusters we wish to find, and because it can be easily adapted to the specific needs of our experiments.…”

Section: Methodsmentioning

confidence: 99%

Building Graph Representations of Deep Vector Embeddings

Garcia-Gasulla¹,

Vilalta²,

Parés³

et al. 2017

Preprint

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Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes. Typically, deep network representations are implemented within vector embedding spaces, which enables the use of traditional machine learning algorithms on top of them. In this short paper we propose the construction of a graph embedding space instead, introducing a methodology to transform the knowledge coded within a deep convolutional network into a topological space (i.e., a network). We outline how such graph can hold data instances, data features, relations between instances and features, and relations among features. Finally, we introduce some preliminary experiments to illustrate how the resultant graph embedding space can be exploited through graph analytics algorithms.

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Section: Methodsmentioning

confidence: 99%

Building Graph Representations of Deep Vector Embeddings

Garcia-Gasulla¹,

Vilalta²,

Parés³

et al. 2017

Preprint

Self Cite

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“…The asynchronous fluid communities algorithm [37] is based on the idea of fluids interacting in an environment, expanding and pushing each other. Its initialization is random, so found communities may vary on different executions.…”

mentioning

confidence: 99%

How to find Orchestrated Trolls?A Case Study on Identifying Polarized Twitter Echo Chambers

Kratzke¹

2023

Preprint

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Background: This study presents a graph-based and purely structural analysis to detect echo chambers on Twitter. Echo chambers are a concern as they can spread misinformation and reinforce harmful stereotypes and biases in social networks. Methods: The study recorded the German-language Twitter stream over two months, recording about 180.000 accounts and their interactions. The study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. Results: The purely structural detection approach was able to identify an echo chamber (red community) that was focused on a few topics with a triad of Anti-Covid, right-wing populism, and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community was much more heterogeneous and showed "normal" communication interaction patterns. Conclusions: The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship.

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