Search and replication in unstructured peer-to-peer networks
ICS '02 Proceedings of the 16th international conference on Supercomputing
Mobility increases the capacity of ad hoc wireless networks
IEEE/ACM Transactions on Networking (TON)
Rank-One Approximation to High Order Tensors
SIAM Journal on Matrix Analysis and Applications
Orthogonal Tensor Decompositions
SIAM Journal on Matrix Analysis and Applications
On the Bursty Evolution of Blogspace
World Wide Web
Spray and wait: an efficient routing scheme for intermittently connected mobile networks
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Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Tensor-CUR decompositions for tensor-based data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing random walk search algorithms in P2P networks
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Weak state routing for large scale dynamic networks
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Description and simulation of dynamic mobility networks
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Building a reference combinatorial model for MANETs
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Fast track article: Connectivity in time-graphs
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Characterizing continuous time random walks on time varying graphs
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Worst-case latency of broadcast in intermittently connected networks
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Dynamic networks are characterized by topologies that vary with time and are represented by time-graphs. The notion of connectivity in time-graphs is fundamentally different than that in static graphs. End-to-end connectivity is achieved opportunistically by store-forward-carry paradigm if the network is so sparse that source-destination pairs are usually not connected by complete paths. In static graphs, it is well known that the network connectivity is tied to the spectral gap of the underlying adjacency matrix of the topology: if the gap is large, the network is well connected and a random walk on this graph has a small hitting time. In this paper, we investigate a similar metric for time-graphs, which indicates how quickly opportunistic methods deliver packets to destinations, speed of convergence in estimating an entity and quickness in the online optimization of protocol parameters, etc. To this end, a time-graph is represented by a 3-mode reachability tensor which yields whether a vertex is reachable from another node within t steps. Our observations from an extensive set of simulations show that the correlation between the expected hitting time of a random walk in the time-graph (following a non-homogenous Markov Chain) and the second singular value of the matrix obtained by unfolding the reachability tensor is significantly large, above 90%.