@inproceedings{latoucherossi2015graph-machine,
TITLE = {{Graphs in machine learning: an introduction}},
AUTHOR = {Latouche, Pierre and Rossi, Fabrice},
booktitle = {Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015)},
address = {Bruges, Belgique},
language = {english},
audience = {international},
year = {2015},
pages = {207--218},
month = {4},
hal= {hal-01166849},
arxiv={1506.06962},
publisherlink = {http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-14.pdf},
entrysubtype={inproceedings-international-conference},
KEYWORDS = {Graph ; Graph Clustering ; Graph Kernel ; Stochastic Block Model},
abstract={Graphs are commonly used to characterise interactions between objects of interest. Because they are based on a straightforward formalism, they are used in many scientific fields from computer science to historical sciences.
In this paper, we give an introduction to some methods relying on graphs for learning. This includes both unsupervised and supervised methods. Unsupervised learning algorithms usually aim at visualising graphs in latent spaces and/or clustering the nodes. Both focus on extracting knowledge from graph topologies. While most existing techniques are only applicable to static graphs, where edges do not evolve through time, recent developments have shown that they could be extended to deal with evolving networks.
In a supervised context, one generally aims at inferring labels or numerical values attached to nodes using both the graph and, when they are available, node characteristics. Balancing the two sources of information can be challenging, especially as they can disagree locally or globally.
In both contexts, supervised and unsupervised, data can be relational (augmented with one or several global graphs) as described above, or graph valued. In this latter case, each object of interest is given as a full graph (possibly completed by other characteristics). In this context, natural tasks include graph clustering (as in producing clusters of graphs rather than clusters of nodes in a single graph), graph classification, etc. },
}