Evolving graphs arise in problems where interrelations between data change over time. We present a breadth first search (BFS) algorithm for evolving graphs that computes the most direct influences between nodes at two different times. Using simple examples, we show that na �ıve unfoldings of adjacency matrices miscount the number of temporal paths. By mapping an evolving graph to an adjacency matrix of an equivalent static graph, we prove that our generalization of the BFS algorithm correctly accounts for paths that traverse both space and time. Finally, we demonstrate how the BFS over evolving graphs can be applied to mine citation networks
Most of the works on learning from networked data assume that the network is static. In this paper w...
Graphs are popularly used to model structural relationships between objects. In many application dom...
Applications such as neuroscience, telecommunication, on-line social networking, transport and retai...
Evolving graphs arise in many different contexts where the interrelations between data elements chan...
Motivated by applications that concern graphs that are evolving and massive in nature, we define a n...
Today's applications process large scale graphs which are evolving in nature. We study new com-\ud p...
In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that descr...
A graph is a mathematical structure for modelling the pairwise relations between objects. This thesi...
Discovery of evolution chains Discovery of change patterns Change mining in networked data a b s t r...
Data mining techniques for understanding how graphs evolve over time have become increasingly import...
International audienceTo describe the dynamics taking place in networks that structurally change ove...
Abstract — Graphs are adept at describing relational data, hence their popularity in fields includin...
In a search graph a node's value may be dependent on the path leading to it. Different paths may lea...
We propose dynamic graph-based relational mining approach to learn structural patterns in graphs or ...
Thesis (Ph.D.), Department of Electrical Engineering and Computer Science, Washington State Universi...
Most of the works on learning from networked data assume that the network is static. In this paper w...
Graphs are popularly used to model structural relationships between objects. In many application dom...
Applications such as neuroscience, telecommunication, on-line social networking, transport and retai...
Evolving graphs arise in many different contexts where the interrelations between data elements chan...
Motivated by applications that concern graphs that are evolving and massive in nature, we define a n...
Today's applications process large scale graphs which are evolving in nature. We study new com-\ud p...
In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that descr...
A graph is a mathematical structure for modelling the pairwise relations between objects. This thesi...
Discovery of evolution chains Discovery of change patterns Change mining in networked data a b s t r...
Data mining techniques for understanding how graphs evolve over time have become increasingly import...
International audienceTo describe the dynamics taking place in networks that structurally change ove...
Abstract — Graphs are adept at describing relational data, hence their popularity in fields includin...
In a search graph a node's value may be dependent on the path leading to it. Different paths may lea...
We propose dynamic graph-based relational mining approach to learn structural patterns in graphs or ...
Thesis (Ph.D.), Department of Electrical Engineering and Computer Science, Washington State Universi...
Most of the works on learning from networked data assume that the network is static. In this paper w...
Graphs are popularly used to model structural relationships between objects. In many application dom...
Applications such as neuroscience, telecommunication, on-line social networking, transport and retai...