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 ad- dress 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 to both the encoder and decoder. 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. Las...
Stochastic block models (SBMs) provide a statistical way modeling network data, especially in repres...
The problem of community detection has received great attention in recent years. Many methods have b...
In the present paper we study a sparse stochastic network enabled with a block structure. The popula...
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 ...
Stochastic Pooling Networks (SPN) were recently introduced as a general conceptual framework for mod...
Classical rate-distortion theory requires specifying a source distribution. Instead, we analyze rate...
A general framework for modeling surprising nonlinear interactions between redundancy and two forms ...
Finding communities in complex networks is a challenging task and one promising approach is the Stoc...
International audienceThe labeled stochastic block model is a random graph model representing networ...
Abstract — Classical rate-distortion theory requires knowledge of an elusive source distribution. In...
We explicitly quantify the empirically observed phenomenon that estimation under a stochastic block ...
International audienceCommunity detection in graphs often relies on ad hoc algorithms with no clear ...
Abstract—Classical rate-distortion theory requires specifying a source distribution. Instead, we ana...
Stochastic block models (SBMs) provide a statistical way modeling network data, especially in repres...
The problem of community detection has received great attention in recent years. Many methods have b...
In the present paper we study a sparse stochastic network enabled with a block structure. The popula...
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 ...
Stochastic Pooling Networks (SPN) were recently introduced as a general conceptual framework for mod...
Classical rate-distortion theory requires specifying a source distribution. Instead, we analyze rate...
A general framework for modeling surprising nonlinear interactions between redundancy and two forms ...
Finding communities in complex networks is a challenging task and one promising approach is the Stoc...
International audienceThe labeled stochastic block model is a random graph model representing networ...
Abstract — Classical rate-distortion theory requires knowledge of an elusive source distribution. In...
We explicitly quantify the empirically observed phenomenon that estimation under a stochastic block ...
International audienceCommunity detection in graphs often relies on ad hoc algorithms with no clear ...
Abstract—Classical rate-distortion theory requires specifying a source distribution. Instead, we ana...
Stochastic block models (SBMs) provide a statistical way modeling network data, especially in repres...
The problem of community detection has received great attention in recent years. Many methods have b...
In the present paper we study a sparse stochastic network enabled with a block structure. The popula...