Temporal PageRank on Social Networks

Hu, Weishu; Zou, Haitao; Gong, Zhiguo

doi:10.1007/978-3-319-26190-4_18

Cited by 18 publications

(10 citation statements)

References 25 publications

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“…To our knowledge, temporal PageRank ( Rozenshtein and Gionis 2016 ) is the only published work about temporal generalizations of PageRank. Other results focus on coarse, static snapshots such as Bonacich’s centrality ( Lerman et al 2010 ), or use temporal information to calculate edges of a static graph ( Hu et al 2015 ; Manaskasemsak et al 2013 ). Finally, another line of research considers updating PageRank in dynamic or online scenarios ( Bahmani et al 2010 ; Bahmani et al 2012 ; Kim and Choi 2015 ; Ohsaka et al 2015 ; Sarma et al 2011 ); however, in these results PageRank is considered a stationary distribution over the current, static graph.…”

Section: Related Resultsmentioning

confidence: 99%

Temporal walk based centrality metric for graph streams

et al. 2018

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A plethora of centrality measures or rankings have been proposed to account for the importance of the nodes of a network. In the seminal study of Boldi and Vigna (2014), the comparative evaluation of centrality measures was termed a difficult, arduous task. In networks with fast dynamics, such as the Twitter mention or retweet graphs, predicting emerging centrality is even more challenging.Our main result is a new, temporal walk based dynamic centrality measure that models temporal information propagation by considering the order of edge creation. Dynamic centrality measures have already started to emerge in publications; however, their empirical evaluation is limited. One of our main contributions is creating a quantitative experiment to assess temporal centrality metrics. In this experiment, our new measure outperforms graph snapshot based static and other recently proposed dynamic centrality measures in assigning the highest time-aware centrality to the actually relevant nodes of the network. Additional experiments over different data sets show that our method perform well for detecting concept drift in the process that generates the graphs.

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Section: Related Resultsmentioning

confidence: 99%

Temporal walk based centrality metric for graph streams

et al. 2018

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“…Temporal graphs, also know as interaction [14,23] or temporal networks [7], are being studied using multiple approaches. One approach is to extend global properties from static graph theory such as page rank [8,22], shortest path [17,24,29], or centrality measures [1,21] to temporal networks and to introduce efficient algorithms to compute them. Other works focus on better understanding the nature and evolution of such temporal graphs.…”

Section: Related Workmentioning

confidence: 99%

“…In this way we can also construct a candidate set {x | ∃(x, tx) ∈ S(a) : t b < tx < t}. [1,8], {b, d}). This seed candidate actually corresponds to the simple cycle in Figure 1b…”

Section: Reverse Reachability Summarymentioning

confidence: 99%

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2scent

Kumar

Calders

2018

Proc. VLDB Endow.

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In interaction networks nodes may interact continuously and repeatedly. Not only which nodes interact is important, but also the order in which interactions take place and the patterns they form. These patterns cannot be captured by solely inspecting the static network of who interacted with whom and how frequently, but also the temporal nature of the network needs to be taken into account. In this paper we focus on one such fundamental interaction pattern, namely a temporal cycle. Temporal cycles have many applications and appear naturally in communication networks. In financial networks, on the other hand, the presence of a temporal cycle could be indicative for certain types of fraud, and in biological networks, feedback loops are a prime example of this pattern type. We present 2SCENT, an efficient algorithms to find all temporal cycles in a directed interaction network. 2SCENT consist of a non-trivial temporal extension of a seminal algorithm for finding cycles in static graphs, preceded by an efficient candidate root filtering technique which can be based on Bloom filters to reduce the memory footprint. We tested 2SCENT on six real-world data sets, showing that it is up to 300 times faster than the only existing competitor and scales up to networks with millions of nodes and hundreds of millions of interactions. Results of a qualitative experiment indicate that different interaction networks may have vastly different distributions of temporal cycles, and hence temporal cycles are able to characterize an important aspect of the dynamic behavior in the networks.

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“…However, we discussed in the introduction, even in these dynamic settings PageRank is defined as a stationary distribution over a static graph (the current graph). Another research direction uses temporal information to calculate weights of edges of a static graph [10,17].…”

Section: Related Workmentioning

confidence: 99%

Temporal PageRank

Rozenshtein

Gionis

2016

Lecture Notes in Computer Science

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PageRank is one of the most popular measures for ranking the nodes of a network according to their importance. However, Page-Rank 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 Page-Rank 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 Page-Rank 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.

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Temporal PageRank on Social Networks

Cited by 18 publications

References 25 publications

Temporal walk based centrality metric for graph streams

Temporal walk based centrality metric for graph streams

2scent

Temporal PageRank

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