Mining Connection Pathways for Marked Nodes in Large Graphs

Akoglu, Leman; Vreeken, Jilles; Tong, Hanghang; Chau, Duen Horng; Tatti, Nikolaj; Faloutsos, Christos

doi:10.1137/1.9781611972832.5

Cited by 36 publications

(58 citation statements)

References 33 publications

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“…Faster specialpurpose approximations have also been studied in the data mining community, e.g., for temporal networks [8]. The most related algorithmic results are those of Akoglu et al [1], who study the problem of finding a good partitioning and connection structure within each part on undirected graphs for a given set of query nodes. Although their purpose is to explore an undirected graph, they map the problem to graph partitioning plus finding Steiner arborescences.…”

Section: Discussion and Related Workmentioning

confidence: 99%

“…There are a number of algorithms that provide good approximation bounds for the directed Steiner problem [2,7,11], and this problem has also been studied recently in the data mining community, e.g., [1,8]. However, Problem 1 is equivalent to the Steiner problem in the case of a uniform background distribution, i.e., when the IC of the edges is constant and hence irrelevant.…”

Section: Algorithms For Finding the Most Interesting Treementioning

confidence: 99%

“…To compare the performance of the heuristics we set up an experiment similar to [1]. We fitted the background model with prior beliefs on the degrees of the network.…”

Section: Comparing the Heuristicsmentioning

confidence: 99%

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Subjectively Interesting Connecting Trees

Adriaens

Lijffijt

Bie

2017

Machine Learning and Knowledge Discovery in Databases

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Abstract. Consider a large network, and a user-provided set of query nodes between which the user wishes to explore relations. For example, a researcher may want to connect research papers in a citation network, an analyst may wish to connect organized crime suspects in a communication network, or an internet user may want to organize their bookmarks given their location in the world wide web. A natural way to show how query nodes are related is in the form of a tree in the network that connects them. However, in sufficiently dense networks, most such trees will be large or somehow trivial (e.g. involving high degree nodes) and thus not insightful. In this paper, we define and investigate the new problem of mining subjectively interesting trees connecting a set of query nodes in a network, i.e., trees that are highly surprising to the specific user at hand. Using information theoretic principles, we formalize the notion of interestingness of such trees mathematically, taking in account any prior beliefs the user has specified about the network. We then propose heuristic algorithms to find the best trees efficiently, given a specified prior belief model. Modeling the user's prior belief state is however not necessarily computationally tractable. Yet, we show how a highly generic class of prior beliefs, namely about individual node degrees in combination with the density of particular sub-networks, can be dealt with in a tractable manner. Such types of beliefs can be used to model knowledge of a partial or total order of the network nodes, e.g. where the nodes represent events in time (such as papers in a citation network). An empirical validation of our methods on a large real network evaluates the different heuristics and validates the interestingness of the given trees.

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Section: Discussion and Related Workmentioning

confidence: 99%

Section: Algorithms For Finding the Most Interesting Treementioning

confidence: 99%

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Subjectively Interesting Connecting Trees

Adriaens

Lijffijt

Bie

2017

Machine Learning and Knowledge Discovery in Databases

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“…We are witnessing the birth of interactive systems that integrate scalable machine learning and data mining algorithms with usable user interfaces [14], [56], [57], such as running graph-based inference algorithms (e.g., Belief Propagation) over million-node graphs in sub-second speed in a background thread, keeping the interface responsive. (Recent research reduces that run time even further [58], [59].)…”

Section: B Scaling Up Graph Sensemaking: Interactive Sensemaking and Smentioning

confidence: 99%

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Pienta

Abello

Kahng

et al. 2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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Abstract-Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant works from multiple research fields -data mining, machine learning, human-computer ineraction, information visualization, information retrieval, and recommender systems -to underline their parallel and complementary contributions to graph sensemaking.We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global). We summarize and compare their strengths and weaknesses, and highlight open challenges. We conclude with future research directions for graph sensemaking.

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“…Most related work in data mining that has inspired our work is [22], which used graph mining for evaluating the quality of search engine results to user queries. Other related graph-based techniques include connection subgraphs [23], [24], [25] that aim to succinctly connect a subset of nodes in a given graph.…”

Section: Related Workmentioning

confidence: 99%

External Evaluation of Topic Models: A Graph Mining Approach

Chan

Akoglu

2013

2013 IEEE 13th International Conference on Data Mining

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Abstract-Given a topic and its top-k most relevant words generated by a topic model, how can we tell whether it is a low-quality or a high-quality topic? Topic models provide a low-dimensional representation of large document corpora, and drive many important applications such as summarization, document segmentation, word-sense disambiguation, etc. Evaluation of topic models is an important issue; since low-quality topics potentially degrade the performance of these applications. In this paper, we develop a graph mining and machine learning approach for the external evaluation of topic models. Based on the graph-centric features we extract from the projection of topic words on the Wikipedia page-links graph, we learn models that can predict the human-perceived quality of topics (based on human judgments), and classify them as high or low quality. Experiments on four real-world corpora show that our approach boosts the prediction performance up to 30% over three baselines of various complexities, and demonstrate the generality of our method to diverse domains. In addition, we provide an interpretation of our models and outline the discriminating characteristics of topic quality.

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Mining Connection Pathways for Marked Nodes in Large Graphs

Cited by 36 publications

References 33 publications

Subjectively Interesting Connecting Trees

Subjectively Interesting Connecting Trees

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

External Evaluation of Topic Models: A Graph Mining Approach

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