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*Candidate Profile: *

Master degree or Engineer in computer science, mathematics or physics with

strong skills in graph algorithms/network science / Data Science/data
mining and programming in


*Financing Institution: *

ANR Contract Coregraphie (ANR-20-CE23-0002)

Application Deadline: June 25, 2021

Contract starts: October 01, 2021

*Supervisors: *

H. Cherifi (LIB, Dijon), H. Séba (LIRIS, Lyon), O. Togni (LIB, Dijon)

*Contacts: *

[log in to unmask], [log in to unmask], olivier.togni@u-

*Subject: *

In this Big Data world, we face the central issue of processing massive
graphs. The approach considered in the ANR project Coregraphie is to build
a summary (or compressed version) of the graph and to query this summary
instead of the original graph.

One can distinguish between lossless and lossy compression techniques.
Lossless compression (or compact coding) decreases the size of the graph
representation without losing any information while controlling the cost
induced by coding on the operations, like, for instance, in WebGraph [BV04].

Lossy compression allows a part of the information (nodes and edges) to be
lost. If the compression is by deleting edges, one speaks of
sparsification. This subclass of lossy compression allows more effective
requests, such as estimating the distance between two nodes [KB+21]. Lossy
compression is mainly accomplished using two approaches: Selecting a
sample, i.e., a sub-graph using different technics (random walks,
propagation, filtering, etc.) [HL13] and grouping nodes/edges
(generalization) [CR15]. In most cases, lossy compression methods are
specialized for one type of request [FL+12].

One major issue for lossy compression is determining to which extent the
compression algorithms damage the initial graph and how this damage can be
measured and controlled. This thesis aims to concentrate on lossy
compression. We plan to investigate the impact of compression methods on
the graph topological properties and/or requests performed. We propose
approaching this issue starting with simple requests such as testing
neighborhoods or proximity between nodes. We will then study more complex
requests such as finding a given size clique, clustering [QK15] or
partitioning the graph into independent sets [T19].

Among further aspects that can be explored are:

· The links with lossless compression and combined approaches;

· Links with structural properties, in particular with some
orders/hierarchies ((k-

shells, k-trusses, modular decomposition, twin-width, etc.);

· Links with community structure and centrality measures [GC+20];

· Compromises between preserving properties and anonymizing [MRT20].

The main application domain is social networks. Indeed, the LIB lab has the

scientific environment to handle massive online social data. Data from
Medicine, Biology, Economics may also be considered. The final goal is to
propose effective tailored compression methods and generic
compression schemes to deal with many types of requests while controlling
the bias induced by the compression.


[BV04] P. Boldi and S. Vigna, The webgraph framework i: Compression
techniques. In Proceedings of the 13th International Conference on World
Wide Web, WWW ’04, pages 595–602, New York, NY, USA, 2004. ACM.

[CR15] J. Casa-Roma, F. Rousseau, Community-preserving generalization of
social networks. In IEEE. 2015 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining (ASONAM).

[S.l.], 2015. p. 1465–1472.

[FL+12] W. Fan, J. Li, X. Wang, and Y. Wu, Query preserving graph
compression. In

Proceedings of the 2012 ACM SIGMOD International Conference on Management of

Data (SIGMOD '12), 2012. DOI: 

[GC+20] Z. Ghalmane,C. Cherifi, H. Cherifi, M. El Hassouni. Extracting
backbones in

weighted modular complex networks. Sci Rep 10, 15539 (2020). 

[HL13] P. Hu and WC. Lau, A survey and taxonomy of graph sampling. 2013.
CoRR abs/1308.5865, 

[KB+21] A. E. Kiouche, J. Baste, M. Haddad and H.Seba. A
Neighborhood-preserving Graph Summarization, 2021, coRR abs/ 2101.11559, .

[MRT20] G. Minello, L. Rossi and A. Torsello, k-Anonymity on Graphs using
the Szemerédi Regularity Lemma, IEEE Transactions on Network Science and
Engineering, 2020, .

[QK15] F. Queyroi and S. Kirgizov. Suppression distance computation for
hierarchical clusterings. Information Processing Letters, Volume 115, Issue
9, 2015.

[T19] O. Togni. Coloring Large Real World Networks : the DSAT-ratio, In
MARAMI 2019, 2019.

[YAA21] M. I. Yousuf, I. Anwer and R.Anwar, Empirical Characterization of

Sampling Algorithms, 2021, coRR abs/2102.07980 , 

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