Discovering descriptive rules in relational dynamic graphs

Nguyen, Kim-Ngan T.; Cerf, Loïc; Plantevit, Marc; Boulicaut, Jean-François

doi:10.3233/ida-120567

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…For example, given that the average house price in Vancouver is 1.1 million dollars as the probe cell, using gradient analysis we can find all the sub-regions of Vancouver where the average house price is 20% higher than 1.1 million dollars. Gradient analysis has been found useful in business intelligence [3,17]. More efficient and effective algorithms were proposed [19].…”

Section: Related Workmentioning

confidence: 99%

Multidimensional benchmarking in data warehouses

Campbell

Mao

Pei

et al. 2017

IDA

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Benchmarking is among the most widely adopted practices in business today. However, to the best of our knowledge, conducting multidimensional benchmarking in data warehouses has not been explored from a technical efficiency perspective. In this paper, we formulate benchmark queries in the context of data warehousing and business intelligence, and develop algorithms to answer benchmark queries efficiently. Our methods employ a few interesting ideas and the state-of-the-art data cube computation techniques to reduce the number of aggregate cells that need to be computed and indexed. An empirical study using the TPC-H and the Weather data sets demonstrates the efficiency and the scalability of our methods.

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Section: Related Workmentioning

confidence: 99%

Multidimensional benchmarking in data warehouses

Campbell

Mao

Pei

et al. 2017

IDA

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“…Berlingerio et al [2] extract patterns based on frequency and derive evolution rules to solve prediction problems [4]. In [23], the authors study dynamic graphs by means of descriptive n-ary association rules.…”

Section: Related Workmentioning

confidence: 99%

What effects topological changes in dynamic graphs?

Kaytoue

Pitarch

Plantevit

et al. 2015

Soc. Netw. Anal. Min.

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To cite this version:Mehdi Kaytoue, Yoann Pitarch, Marc Plantevit, Céline Robardet. What effects topological changes in dynamic graphs?: Elucidating relationships between vertex attributes and the graph structure. Social Abstract To describe the dynamics taking place in networks that structurally change over time, we propose an approach to search for vertex attributes whose value changes impact the topology of the graph. In several applications, it appears that the variations of a group of attributes are often followed by some structural changes in the graph that one may assume they generate. We formalize the triggering pattern discovery problem as a method jointly rooted in sequence mining and graph analysis. We apply our approach on three real-world dynamic graphs of different natures -a co-authoring network, an airline network, and a social bookmarking system -assessing the relevancy of the triggering pattern mining approach.

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“…Regarding the research on dynamic graph mining, current algorithms consider a dynamic graph with only edges insertions or deletions, i.e., the time series of graphs share the same set of nodes over time (see, e.g., [14]), or in which nodes and edges are only added and never deleted (see, e.g., [2]). In our approach, however, there is no information about the correspondence between the nodes in one graph (video frame) and those in the others.…”

Section: Related Workmentioning

confidence: 99%

Mining spatiotemporal patterns in dynamic plane graphs

Prado

Jeudy

Fromont

et al. 2013

IDA

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Dynamic graph mining is the task of searching for subgraph patterns that capture the evolution of a dynamic graph. In this paper, we are interested in mining dynamic graphs in videos. A video can be regarded as a dynamic graph, whose evolution over time is represented by a series of plane graphs, one graph for each video frame. As such, subgraph patterns in this series may correspond to objects that frequently appear in the video. Furthermore, by associating spatial information to each of the nodes in these graphs, it becomes possible to track a given object through the video in question. We present, in this paper, two plane graph mining algorithms, called Plagram and DyPlagram, for the extraction of spatiotemporal patterns. A spatiotemporal pattern is a set of occurrences of a given subgraph pattern which are not too far apart w.r.t time nor space. Experiments demonstrate that our algorithms are effective even in contexts where general-purpose algorithms would not provide the complete set of frequent subgraphs. We also show that they give promising results when applied to object tracking in videos.

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Discovering descriptive rules in relational dynamic graphs

Cited by 9 publications

References 34 publications

Multidimensional benchmarking in data warehouses

Multidimensional benchmarking in data warehouses

What effects topological changes in dynamic graphs?

Mining spatiotemporal patterns in dynamic plane graphs

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