The stochastic block model (SBM) is extensively used to model networks in which users belong to certain communities. In recent years, the study of information-theoretic compression of such networks has gained attention, with works primarily focusing on lossless compression. In this work, we address the lossy compression of SBM graphs by characterizing the rate-distortion function under a Hamming distortion constraint. Specifically, we derive the conditional rate-distortion function of the SBM with community membership as side information. We approach this problem as the classical Wyner-Ziv lossy problem by minimising mutual information of the graph and its reconstruction conditioned on community labels. Lastly, we also derive the rate-disto...
Stochastic Pooling Networks (SPN) were recently introduced as a general conceptual framework for mod...
A general framework for modeling surprising nonlinear interactions between redundancy and two forms ...
We consider the community detection problem in sparse random hypergraphs under the non-uniform hyper...
The stochastic block model (SBM) is extensively used to model networks in which users belong to cert...
Motivated by the prevalent data science applications of processing and mining large-scale graph data...
With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of ...
International audienceThe labeled stochastic block model is a random graph model representing networ...
Finding communities in complex networks is a challenging task and one promising approach is the Stoc...
In the present paper we study a sparse stochastic network enabled with a block structure. The popula...
The problem of community detection has received great attention in recent years. Many methods have b...
Thesis (Ph.D.)--University of Washington, 2017-08In this thesis, two problems in social networks wil...
Stochastic block models (SBMs) provide a statistical way modeling network data, especially in repres...
© 2013 IEEE. Stochastic block models (SBMs) have been playing an important role in modeling clusters...
Stochastic block models (SBMs) provide a statistical way modeling network data, especially in repres...
We explicitly quantify the empirically observed phenomenon that estimation under a stochastic block ...
Stochastic Pooling Networks (SPN) were recently introduced as a general conceptual framework for mod...
A general framework for modeling surprising nonlinear interactions between redundancy and two forms ...
We consider the community detection problem in sparse random hypergraphs under the non-uniform hyper...
The stochastic block model (SBM) is extensively used to model networks in which users belong to cert...
Motivated by the prevalent data science applications of processing and mining large-scale graph data...
With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of ...
International audienceThe labeled stochastic block model is a random graph model representing networ...
Finding communities in complex networks is a challenging task and one promising approach is the Stoc...
In the present paper we study a sparse stochastic network enabled with a block structure. The popula...
The problem of community detection has received great attention in recent years. Many methods have b...
Thesis (Ph.D.)--University of Washington, 2017-08In this thesis, two problems in social networks wil...
Stochastic block models (SBMs) provide a statistical way modeling network data, especially in repres...
© 2013 IEEE. Stochastic block models (SBMs) have been playing an important role in modeling clusters...
Stochastic block models (SBMs) provide a statistical way modeling network data, especially in repres...
We explicitly quantify the empirically observed phenomenon that estimation under a stochastic block ...
Stochastic Pooling Networks (SPN) were recently introduced as a general conceptual framework for mod...
A general framework for modeling surprising nonlinear interactions between redundancy and two forms ...
We consider the community detection problem in sparse random hypergraphs under the non-uniform hyper...