Human Interactive Patterns in Temporal Networks

Zhang, Yi-Qing; Li, Xiang; Xu, Jiajin; Vasilakos, Athanasios V.

doi:10.1109/tsmc.2014.2360505

Cited by 59 publications

(9 citation statements)

References 39 publications

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“…Zhao et al [47] devised communication motifs to study the information propagation in human communication networks, such as call detail records (CDR) and Facebook wall post interactions. Zhang et al [46] introduced motif-driven analysis for human interactions including phone messages, face-to-face interactions, and sexual contacts. Liu et al studied patent oppositions and collaborations [27] and financial transaction networks [25] by using temporal motifs.…”

Section: Temporal Motifsmentioning

confidence: 99%

“…Temporal motifs, also known as higher-order structures, are an important building block in temporal networks [14,21,26,32,34,38]. Temporal motif-based analysis has been used for many applications including cattle trade movements [3], editor interactions in Wikipedia [16], mobile communication networks [22,24], and human interactions [46]. Temporal motifs are also a promising and effective tool for temporal graph generation.…”

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confidence: 99%

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Using Motif Transitions for Temporal Graph Generation

Liu

Sarıyüce

2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Graph generative models are highly important for sharing surrogate data and benchmarking purposes. Real-world complex systems often exhibit dynamic nature, where the interactions among nodes change over time in the form of a temporal network. Most temporal network generation models extend the static graph generation models by incorporating temporality in the generation process. More recently, temporal motifs are used to generate temporal networks with better success. However, existing models are often restricted to a small set of predefined motif patterns due to the high computational cost of counting temporal motifs. In this work, we develop a practical temporal graph generator, Motif Transition Model (MTM), to generate synthetic temporal networks with realistic global and local features. Our key idea is modeling the arrival of new events as temporal motif transition processes. We first calculate the transition properties from the input graph and then simulate the motif transition processes based on the transition probabilities and transition rates. We demonstrate that our model consistently outperforms the baselines with respect to preserving various global and local temporal graph statistics and runtime performance.

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Section: Temporal Motifsmentioning

confidence: 99%

mentioning

confidence: 99%

Using Motif Transitions for Temporal Graph Generation

Liu

Sarıyüce

2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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“…However, even after this simplification the extraction of actionable information on financial markets is not a trivial task, firstly because we have no prior knowledge what these signals would look like, and must thus analyze movements within the system, identify recurrent sequences that appear, and use these to infer the rules of information processing within financial markets. Such an analysis has been carried out in various fields to analyze various complex systems [26][27][28][29][30], and we ourselves have done so for natural languages [31] and teaching practices [32]. Secondly, the very many signals overlap in time to mask each other, and more importantly, participants hide their intentions as they trade.…”

Section: Introductionmentioning

confidence: 99%

Identifying Actionable Serial Correlations in Financial Markets

Cheong

Lee

Li³

et al. 2021

Front. Appl. Math. Stat.

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Financial markets are complex systems where information processing occurs at multiple levels. One signature of this information processing is the existence of recurrent sequences. In this paper, we developed a procedure for finding these sequences and a process of statistical significance testing to identify the most meaningful ones. To do so, we downloaded daily closing prices of the Dow Jones Industrial Average component stocks, as well as various assets like stock market indices, United States government bonds, precious metals, commodities, oil and gas, and foreign exchange. We mapped each financial instrument to a letter and their upward movements to words, before testing the frequencies of these words against a null model obtained by reshuffling the empirical time series. We then identify market leaders and followers from the statistically significant words in different cross sections of financial instruments, and interpret actionable trends that can be traded upon.

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“…The characterization and modelling of time-varying networks were put into practice because of availability of large data of real-time tracking of human interactions and social relationships. In this context, considering interactions in dynamic networks as a time series of events, a number of recent works focused on the statistics of events and inter-events, [7][8][9][10][11][12][13] and investigated their influences on diffusion and spreading processes. [14][15][16][17][18][19][20][21] On the one hand, it has been shown that the distribution of inter-event times of some communication systems follows non-Poissonian, heavy-tailed distributions, which results in bursty patterns of concurrency and duration of interactions.…”

Section: Introductionmentioning

confidence: 99%

Time-varying networks based on activation and deactivation mechanisms

et al. 2017

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A class of models for activity-driven networks is proposed in which nodes vary in two states: active and inactive. Only active nodes can receive links from others which represent instantaneous dynamical interactions. The evolution of the network couples the addition of new nodes and state transitions of old ones. The active group changes with activated nodes entering and deactivated ones leaving. A general differential equation framework is developed to study the degree distribution of nodes of integrated networks where four different schemes are formulated.

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Human Interactive Patterns in Temporal Networks

Cited by 59 publications

References 39 publications

Using Motif Transitions for Temporal Graph Generation

Using Motif Transitions for Temporal Graph Generation

Identifying Actionable Serial Correlations in Financial Markets

Time-varying networks based on activation and deactivation mechanisms

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