Mining Regular Patterns in Weighted-Directed Networks

Gupta, Anand; Thakur, Hardeo Kumar; Kishore, P. Venkata

doi:10.1109/icit.2014.44

Cited by 7 publications

(11 citation statements)

References 8 publications

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“…A graph G here denotes a dynamic directed graph G = (V, E, F), where V denotes the node set, E = {< u, v > | u, v ÎV} denotes the edge set where < u, v > is an ordered pair of nodes, and F is the mapping, assigning direction to edges as defined in (Gupta, Thakur, & Kishore, 2014). In this paper, occurrence for the ordered pair of nodes {< u, v >Î E} representing a directed edge is represented such that '1' corresponds to the presence of the edge in the time-slice under consideration, and a '0' represents its absence.…”

Section: Notations and Definitionsmentioning

confidence: 99%

“…Definition 2: Occurrence sequence-As defined in (Gupta, Thakur, & Kishore, 2014), the occurrence sequence of an edge 'e' is a sequence of 1s and 0s with a length T such that if 'e' appears at time-slice 't', the 't' th position in its occurrence sequence is 1, otherwise it is 0.…”

Section: Notations and Definitionsmentioning

confidence: 99%

“…Definition 5: Summary graph -Summary graph of a dynamic directed graph G =< G 1 ,G 2 ,..,G T >, as put forward in (Gupta, Thakur, & Kishore, 2014), is defined as sets of G s = V s , E s , L e , where V s =V (V 1 ÈV 2 È…ÈV T ), E s = E 1 ÈE 2 È…È E T , and Le is the set of labels of E s , which maps in each set of G s , each edge e to an occurrence sequence of length T. This definition can be extended to nodes, where in each set of G s , each node v is mapped to an indegree and outdegree sequence of length T.…”

Section: Notations and Definitionsmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Regular Patterns in Un-Weighted Directed Graphs

Gupta

Thakur

Jain

et al. 2021

International Journal of Information Retrieval Research

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Time evolving networks tend to have an element of regularity. This regularity is characterized by existence of repetitive patterns in the data sequences of the graph metrics. As per our research, the relevance of such regular patterns to the network has not been adequately explored. Such patterns in certain data sequences are indicative of properties like popularity, activeness etc. which are of vital significance for any network. These properties are closely indicated by data sequences of graph metrics - degree prestige, degree centrality and occurrence. In this paper, (a) an improved mining algorithm has been used to extract regular patterns in these sequences, and (b) a methodology has been proposed to quantitatively analyse the behavior of the obtained patterns. To analyze this behavior, a quantification measure coined as "Sumscore" has been defined to compare the relative significance of such patterns. The patterns are ranked according to their Sumscores and insights are then drawn upon it. The efficacy of this method is demonstrated by experiments on two real world datasets.

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Section: Notations and Definitionsmentioning

confidence: 99%

Section: Notations and Definitionsmentioning

confidence: 99%

Section: Notations and Definitionsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Regular Patterns in Un-Weighted Directed Graphs

Gupta

Thakur

Jain

et al. 2021

International Journal of Information Retrieval Research

Self Cite

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“…In order to prove the usefulness of the proposed method, the experiments on Enron Email dataset has been conducted using a method [14] which ignores noise and compared the results with those obtained from the proposed method. The results obtained are summarized in Table 1.…”

Section: ) Enron Email Datasetmentioning

confidence: 99%

“…The pattern here would have been "aaa" if the products A and B would have been sold on the 9 th day as well. This pattern (Quasi regular), though important, would have been lost if mined by the existing algorithms [14,7]. Our work tries to fill the gap that has been there due to the lack of focus on noisy aspect of dynamic datasets.…”

Section: Introductionmentioning

confidence: 99%

Mining Maximal Quasi Regular Patterns in Weighted Dynamic Networks

Thakur¹,

Gupta²,

Jain³

et al. 2017

IJITCS

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Abstract-Interactions appearing regularly in a network may be disturbed due to the presence of noise or random occurrence of events at some timestamps. Ignoring them may devoid us from having better understanding of the networks under consideration. Therefore, to solve this problem, researchers have attempted to find quasi/quasiregular patterns in non-weighted dynamic networks. To the best of our knowledge, no work has been reported in mining such patterns in weighted dynamic networks. So, in this paper we present a novel method which mines maximal quasi regular patterns on structure (MQRPS) and maximal quasi regular patterns on weight (MQRPW) in weighted dynamic networks. Also, we have provided a relationship between MQRPW and MQRPS which facilitates in the running of the proposed method only once, even when both are required and thus leading to reduction in computation time. Further, the analysis of the patterns so obtained is done to gain a better insight into their nature using four parameters, viz. modularity, cliques, most commonly used centrality measures and intersection. Experiments on Enron-email and a synthetic dataset show that the proposed method with relationship and analysis is potentially useful to extract previously unknown vital information.

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