Over the last few years, there has been considerable amount of study and work on developing algorithms for processing massive graphs in the data stream model. Storing massive graphs in the memory of a single machine is not practical which is what the motivation behind data stream algorithms. To obtain space and time efficient algorithms, we develop streaming/semi-streaming algorithms where it is reasonable to assume that the input graph arrives as a stream of edges. We can process the input in either one or multiple passes and the working memory space is restricted
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
Over the last few years, there has been considerable amount of study and work on developing algorith...
Over the last decade, there has been considerable in-terest in designing algorithms for processing m...
Over the last decade, there has been considerable in-terest in designing algorithms for processing m...
Motivated by the trend to outsource work to commercial cloud computing services, we consider a varia...
Massive graphs arise in a many scenarios, for example, traffic data analysis in large networks, larg...
www.elsevier.com/locate/tcs We formalize a potentially rich new streaming model, the semi-streaming ...
Massive graphs arise in a many scenarios, for example, traffic data analysis in large networks, larg...
Massive graphs arise in a many scenarios, for example, traffic data analysis in large networks, larg...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
We formalize a potentially rich new streaming model, the semi-streaming model, that we believe is ne...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
Over the last few years, there has been considerable amount of study and work on developing algorith...
Over the last decade, there has been considerable in-terest in designing algorithms for processing m...
Over the last decade, there has been considerable in-terest in designing algorithms for processing m...
Motivated by the trend to outsource work to commercial cloud computing services, we consider a varia...
Massive graphs arise in a many scenarios, for example, traffic data analysis in large networks, larg...
www.elsevier.com/locate/tcs We formalize a potentially rich new streaming model, the semi-streaming ...
Massive graphs arise in a many scenarios, for example, traffic data analysis in large networks, larg...
Massive graphs arise in a many scenarios, for example, traffic data analysis in large networks, larg...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
We formalize a potentially rich new streaming model, the semi-streaming model, that we believe is ne...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...
International audienceWe introduce a novel algorithm to perform graph clustering in the edge streami...