Inferring Directed Static Networks of Influence from Undirected Temporal Networks

Takaguchi, Taro; Sato, N.; Yano, Kazuo; Masuda, Naoki

doi:10.1109/compsac.2013.24

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…3(b) with ten links present out of twelve possible. To remedy this some studies put a higher requirement on a directed link than just one time-respecting path, and defines directed "influence networks" [287,300].…”

Section: Reachability and Influence Graphsmentioning

confidence: 99%

Modern temporal network theory: a colloquium

2015

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Abstract. The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.

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Section: Reachability and Influence Graphsmentioning

confidence: 99%

Modern temporal network theory: a colloquium

2015

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“…Features constrained: the sequence of local distributions of interevent durations on each link, \bfitpi \scrL (\Delta \bfittau ) = ([\Delta \tau m (i,j) ] m\in \scrM (i,j) ) (i,j)\in \scrL ; the sequence of times of the first event on each link, t 1 = (t 1 (i,j) ) (i,j)\in \scrL . References: [94,95] (shuffled interevent intervals); section 7. See Figure 4.11.…”

Section: P[p \Scrl (\Theta )]mentioning

confidence: 99%

“…They have been used to study how given temporal network features affect other node-or interaction-level features [49,40,41,50,63], and how the features affect dynamical processes unfolding in the network [42,76,58,89,48,18,37,93,29,43,4,10,98,13,79,19,99,27] as well as the network's controllability [73,52,106]. Systems studied using temporal network RRMs include human face-to-face interactions and physical proximity [94,89,95,18,93,37,4,99,13,96,27,97,10,98,27]; prostitution networks [76,37,13,27]; functional connections in the brain [100,97,92]; human mobility…”

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confidence: 99%

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Randomized Reference Models for Temporal Networks

Gauvin¹,

Génois²,

Karsai³

et al. 2022

SIAM Rev.

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Many dynamical systems can be successfully analyzed by representing them as networks.Empirically measured networks and dynamic processes that take place in these situations show heterogeneous, non-Markovian, and intrinsically correlated topologies and dynamics. This makes their analysis particularly challenging. Randomized reference models (RRMs) have emerged as a general and versatile toolbox for studying such systems. Defined as random networks with given features constrained to match those of an input (empirical) network, they may, for example, be used to identify important features of empirical networks and their effects on dynamical processes unfolding in the network. RRMs are typically implemented as procedures that reshuffle an empirical network, making them very generally applicable. However, the effects of most shuffling procedures on network features remain poorly understood, rendering their use nontrivial and susceptible to misinterpretation. Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs) that sample networks with uniform probability. Focusing on temporal networks, we survey applications of MRRMs found in the literature, and we use this framework to build a taxonomy of MRRMs that proposes a canonical naming \ast

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Prediction of dissolved gas in power transformer oil based on random forests algorithm

Deng

Xiong

Zhu

et al. 2015

2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)

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In order to adopt reasonable measures to anticipate and avoid possible internal failures in power transformers, accurate prediction of power transformer oil dissolved gas trends is useful. It is necessary to realize the condition based maintenance of power transformers, and the prediction of dissolved gas in the oil is solid foundation. The gas concentration is affected by many factors, so prediction model built by reasonable selection of the larger correlation factors will help to improve prediction accuracy. Random forests algorithm is used to predict the gas trends and the prediction performance evaluation is realized by appropriate evaluation indexes. By comparison with support vector machine prediction, the advantage of random forests method for power transformer oil dissolved gas prediction is proved.

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Inferring Directed Static Networks of Influence from Undirected Temporal Networks

Cited by 3 publications

References 8 publications

Modern temporal network theory: a colloquium

Modern temporal network theory: a colloquium

Randomized Reference Models for Temporal Networks

Prediction of dissolved gas in power transformer oil based on random forests algorithm

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