[DMANET] CFP Similarity Search in Graph-Structured Data (Special Session SISAP 2021)

14th International Conference on Similarity Search and Applications
(SISAP) 2021 Special Session:

https://sisap.org/2021/specialsessions.html


Similarity Search in Graph-Structured Data (SISEG)
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Graphs are a versatile data structure for representing structured
objects such as molecules in computational drug discovery, proteins and
their interaction networks in bioinformatics, scene graphs in computer
vision, social networks, and knowledge graphs. Analyzing the large
amounts of data in these domains often involves defining a similarity
measure on graphs (or nodes). The complex structure and heterogeneity of
the graphs appearing in real-world applications makes their comparison
challenging. Various concepts for this have been proposed including
general-purpose and domain-specific approaches. These can be divided
into techniques based on graph matching and graph embedding. Graph
matching refers to methods finding a mapping between the nodes of two
graphs that preserves the adjacency structure. Widely used methods are
based on the graph edit distance, the maximum common subgraph problem,
and network alignment. Similarity search regarding measures from graph
matching is challenging, since the underlying graph problems are
typically NP-hard, and the properties of a metric are not necessarily
satisfied but depend on subtle differences in definition. Graph
embedding methods map graphs into a vector space, such that similar
graphs are mapped to close vectors. This renders standard approaches
possible for graph data and significantly simplifies the problem of
similarity search. Various general-purpose embedding techniques exist
including graph kernels and graph neural networks developed for machine
learning with graphs. Moreover, application-specific techniques have a
long history, e.g., chemical fingerprints for representing molecules.
With the growing amount of available heterogeneous graph-structured
data, similarity search becomes increasingly important.


Topics of interest for this special session include, but are not limited to:

* Similarity measures for graphs and their properties
* Indexing methods for graphs
* Similarity in learning and mining with graphs
* Applications of graph similarity


Deadlines:

Submission deadline May 24, 2021 (AoE)
Paper notification July 26, 2021
Camera-ready due August 8, 2021
Conference September 29 -- October 1, 2021
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