peer-reviewedWe show that the community structure of a network can be used as a coarse version of its embedding in a hidden space with hyperbolic geometry. The finding emerges from a systematic analysis of several real-world and synthetic networks. We take advantage of the analogy for reinterpreting results originally obtained through network hyperbolic embedding in terms of community structure only. First, we show that the robustness of a multiplex network can be controlled by tuning the correlation between the community structures across di erent layers. Second, we deploy an e cient greedy protocol for network navigability that makes use of routing tables based on community structure
The investigation of the hidden metric space behind complex network topologies is a fervid topic in ...
Abstract. What do real communities in social networks look like? Com-munity detection plays a key ro...
Two common features of many large real networks are that they are sparse and that they have strong c...
We show that the community structure of a network can be used as a coarse version of its embedding i...
We show that the community structure of a network can be used as a coarse version of its embedding i...
Over the last decade, random hyperbolic graphs have proved successful in providing geometric explana...
Abstract A remarkable approach for grasping the relevant statistical features of real networks with ...
Network science is driven by the question which properties large real-world networks have and how we...
Recent years have shown a promising progress in understanding geometric underpinnings behind the st...
We introduce Mercator, a reliable embedding method to map real complex networks into their hyperbo...
Network embedding is a frontier topic in current network science. The scale-free property of complex...
Through detailed analysis of scores of publicly available data sets corresponding to a wide range of...
Network embedding has recently attracted lots of attentions in data mining. Existing network embeddi...
AbstractSocial networks, as well as many other real-world networks, exhibit overlapping community st...
Heterogeneous information network (HIN) embedding, aiming to project HIN into a low-dimensional spac...
The investigation of the hidden metric space behind complex network topologies is a fervid topic in ...
Abstract. What do real communities in social networks look like? Com-munity detection plays a key ro...
Two common features of many large real networks are that they are sparse and that they have strong c...
We show that the community structure of a network can be used as a coarse version of its embedding i...
We show that the community structure of a network can be used as a coarse version of its embedding i...
Over the last decade, random hyperbolic graphs have proved successful in providing geometric explana...
Abstract A remarkable approach for grasping the relevant statistical features of real networks with ...
Network science is driven by the question which properties large real-world networks have and how we...
Recent years have shown a promising progress in understanding geometric underpinnings behind the st...
We introduce Mercator, a reliable embedding method to map real complex networks into their hyperbo...
Network embedding is a frontier topic in current network science. The scale-free property of complex...
Through detailed analysis of scores of publicly available data sets corresponding to a wide range of...
Network embedding has recently attracted lots of attentions in data mining. Existing network embeddi...
AbstractSocial networks, as well as many other real-world networks, exhibit overlapping community st...
Heterogeneous information network (HIN) embedding, aiming to project HIN into a low-dimensional spac...
The investigation of the hidden metric space behind complex network topologies is a fervid topic in ...
Abstract. What do real communities in social networks look like? Com-munity detection plays a key ro...
Two common features of many large real networks are that they are sparse and that they have strong c...