Special Issue "Community Detection and Network Embedding"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: 31 May 2021.
Interests: parameterized algorithms for graph modification problems; practical applications of complex network techniques; community detection; graph embeddings
Interests: natural language processing; complex networks; community detection and clustering; word and graph embedding
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.