Special Issue "Efficient Graph Algorithms in Machine Learning"
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".
Deadline for manuscript submissions: closed (6 July 2020).
Interests: data analysis; machine learning; signal processing; graph algorithms
There is currently a large gap between the size of the graph data we possess and the machine learning algorithms we use to process them. For instance, though social networks can grow to include many millions of nodes and billions of edges, most machine learning experiments being done with graph data are limited to graphs that are many magnitudes smaller. This is particularly problematic in the view that the capacity of most learning algorithms to generalize beyond the training set increases monotonically with the size of the data they are trained with. We argue that to fulfill the potential of machine learning on graph data, we need learning algorithms that circumvent computational and memory complexity bottlenecks without compromising solution quality. This special issue aims to gather such research contributions.
Both original contributions and review articles will be considered. Submitted articles may focus on any machine learning problem involving graph data, such as:
- semi-supervised learning (node and edge classification)
- graph classification and embedding (e.g., with graph neural networks or graph kernels)
- unsupervised learning problems (e.g. clustering/community detection, dimensionality reduction, compression, link prediction)
- graph signal processing (filtering, sampling, fast transforms, inverse problems on graphs)
- graph inference and construction
- graph decompositions
- graph recommender systems
- generative models for graphs and graph data
- new application domains (learnable simulators, protein interaction prediction, brain network analysis, point cloud processing)
- learned heuristics for hard graph-theoretic problems
They may also utilize any approximation or heuristic cost-saving scheme, such as sampling (e.g., coresets), structure exploitation (e.g., sparsity, communities), randomized linear algebra, sparsification, coarsening, etc.
Articles that provide strong evidence (theoretical or empirical) of algorithmic efficiency beyond the state of the art will be of particular interest.
The considered gains may be in terms of computational complexity, space complexity, sample complexity, or opportunity for parallelism.
Dr. Andreas Loukas
Dr. Nicolas Tremblay
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.
- machine learning on graphs
- graph signal processing
- geometric deep learning
- graph reduction
- graph inference