Algorithms --- Special Issue "Community Detection and Network Embedding"

Computer Networks & Wireless Communication Engineering & Computer Science (General) Theoretical Computer Science

Over the last several decades, the rapid growth of storage facilities and the advent of the internet and its usage have provided a huge amount of interconnected data. This amount of data brought two major challenges to the network science research field: community detection and network embedding. For the former, the aim is to group nodes of a given network into highly connected subgraphs denominated as communities, with few connections appearing between communities. Detecting the community structure of networks is a way to characterize the graph at its mesoscopic level, thus splitting huge networks into consistent sets of smaller data. In a similar way, network embedding aims at representing nodes of a network in a low-dimensional space while encompassing its main structural properties. These representations can then be processed in reduced time and space.

In this context of big data, community detection and network embedding have thus gained a lot of attention, leading to the development of many algorithms and frameworks. However, many challenges remain in both domains.

As it is very challenging to detect communities in a network, there is still a need for high-performing scalable algorithms. Furthermore, most community detection algorithms focus on partitions when overlapping structures are more realistic in many real-life situations. Besides, dynamic networks have gained attention recently, and are very challenging. Finally, multiplex and heterogeneous networks still present many difficulties.

Besides the need for performing frameworks on classical tasks such as link prediction, network embedding algorithms are often computationally expensive, which brings ecological and durability issues. Furthermore, the fairness and interpretability of the results of such approaches are still interesting ongoing questions.

Finally, if practical applications of such techniques have been proposed in various domains, the connections between these issues and natural language processing, signal and image processing still need to be further investigated.

The purpose of this Special Issue is thus to emphasize work focusing on theoretical and practical contributions to community detection or network embedding . The Special Issue will pay great attention to the scalability of the algorithm, the fairness, interpretability and reproducibility of the results, as well as to ecological considerations. Last but not least, this Special Issue will not promote performance over original and meaningful ideas.

This Special Issue is opened but not limited to the following topics:

  • Community detection algorithms

  • Overlapping communities

  • Incremental community detection

  • Directed, dynamic, multiplex graphs

  • Network embedding

  • Fairness and interpretability in network science

  • Image and signal processing using graph techniques

  • Natural language processing using graph techniques

Dr. Anthony Perez

Dr. Nicolas Dugué

Guest Editors


Manuscript Submission Information


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