TY - GEN
T1 - Temporal PageRank
AU - Rozenshtein, Polina
AU - Gionis, Aristides
N1 - | openaire: EC/H2020/654024/EU//SoBigData
PY - 2016
Y1 - 2016
N2 - PageRank is one of the most popular measures for ranking the nodes of a network according to their importance. However, PageRank is defined as a steady state of a random walk, which implies that the underlying network needs to be fixed and static. Thus, to extend PageRank to networks with a temporal dimension, the available temporal information has to be judiciously incorporated into the model. Although numerous recent works study the problem of computing PageRank on dynamic graphs, most of them consider the case of updating static PageRank under node/edge insertions/deletions. In other words, PageRank is always defined as the static PageRank of the current instance of the graph. In this paper we introduce temporal PageRank, a generalization of PageRank for temporal networks, where activity is represented as a sequence of time-stamped edges. Our model uses the random-walk interpretation of static PageRank, generalized by the concept of temporal random walk. By highlighting the actual information flow in the network, temporal PageRank captures more accurately the network dynamics. A main feature of temporal PageRank is that it adapts to concept drifts: the importance of nodes may change during the lifetime of the network, according to changes in the distribution of edges. On the other hand, if the distribution of edges remains constant, temporal PageRank is equivalent to static PageRank. We present temporal PageRank along with an efficient algorithm, suitable for online streaming scenarios. We conduct experiments on various real and semi-real datasets, and provide empirical evidence that temporal PageRank is a flexible measure that adjusts to changes in the network dynamics. The data and software related to this paper are available at https://github.com/polinapolina/temporal-pagerank.
AB - PageRank is one of the most popular measures for ranking the nodes of a network according to their importance. However, PageRank is defined as a steady state of a random walk, which implies that the underlying network needs to be fixed and static. Thus, to extend PageRank to networks with a temporal dimension, the available temporal information has to be judiciously incorporated into the model. Although numerous recent works study the problem of computing PageRank on dynamic graphs, most of them consider the case of updating static PageRank under node/edge insertions/deletions. In other words, PageRank is always defined as the static PageRank of the current instance of the graph. In this paper we introduce temporal PageRank, a generalization of PageRank for temporal networks, where activity is represented as a sequence of time-stamped edges. Our model uses the random-walk interpretation of static PageRank, generalized by the concept of temporal random walk. By highlighting the actual information flow in the network, temporal PageRank captures more accurately the network dynamics. A main feature of temporal PageRank is that it adapts to concept drifts: the importance of nodes may change during the lifetime of the network, according to changes in the distribution of edges. On the other hand, if the distribution of edges remains constant, temporal PageRank is equivalent to static PageRank. We present temporal PageRank along with an efficient algorithm, suitable for online streaming scenarios. We conduct experiments on various real and semi-real datasets, and provide empirical evidence that temporal PageRank is a flexible measure that adjusts to changes in the network dynamics. The data and software related to this paper are available at https://github.com/polinapolina/temporal-pagerank.
KW - Dynamic graphs
KW - Graph mining
KW - Interaction networks
KW - PageRank
KW - Social-network analysis
KW - Time-evolving networks
UR - http://www.scopus.com/inward/record.url?scp=84988564512&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46227-1_42
DO - 10.1007/978-3-319-46227-1_42
M3 - Conference article in proceedings
AN - SCOPUS:84988564512
SN - 9783319462264
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 674
EP - 689
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
PB - Springer
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Y2 - 19 September 2016 through 23 September 2016
ER -