Inferring social network user profiles using a partial social graph

Dougnon, Raïssa Yapan; Fournier-Viger, Philippe; Lin, Jerry Chun‐Wei; Nkambou, Roger

doi:10.1007/s10844-016-0402-y

Cited by 22 publications

(10 citation statements)

References 36 publications

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“…e dataset [41] contains anonymous data of the whole network, including gender, age, hobbies, interests, and education. In this paper, the relationship of friends in the data is selected to build the network, which contains 1,632,803 nodes and 30,622,564 edges.…”

Section: Large-scale Datasetsmentioning

confidence: 99%

An Improved Louvain Algorithm for Community Detection

Zhang

Fei

Song

et al. 2021

Mathematical Problems in Engineering

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Social network analysis has important research significance in sociology, business analysis, public security, and other fields. The traditional Louvain algorithm is a fast community detection algorithm with reliable results. The scale of complex networks is expanding larger all the time, and the efficiency of the Louvain algorithm will become lower. To improve the detection efficiency of large-scale networks, an improved Fast Louvain algorithm is proposed. The algorithm optimizes the iterative logic from the cyclic iteration to dynamic iteration, which speeds up the convergence speed and splits the local tree structure in the network. The split network is divided iteratively, then the tree structure is added to the partition results, and the results are optimized to reduce the computation. It has higher community aggregation, and the effect of community detection is improved. Through the experimental test of several groups of data, the Fast Louvain algorithm is superior to the traditional Louvain algorithm in partition effect and operation efficiency.

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Section: Large-scale Datasetsmentioning

confidence: 99%

An Improved Louvain Algorithm for Community Detection

Zhang

Fei

Song

et al. 2021

Mathematical Problems in Engineering

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“…Knowledge graphs are fundamental in several research areas, such as for instance pattern mining [19], social network analysis [11], etc.. In this work the focus is on semantic relatedness, which is a key feature in Word Disambiguation [61], Entity Linking [42], Recommendation Systems [41], Data Mining [50], Information Retrieval [33], Question Answering [64], etc.…”

Section: Introductionmentioning

confidence: 99%

Semantic relatedness in DBpedia: A comparative and experimental assessment

Formica

Taglino

2023

Information Sciences

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“…§ Corresponding Author further help to understand users' characteristics and assist for several tasks such as user profile inference [7], [8]. A second example is telecommunication networks where devices (vertices) are connected by telecommunication links (edges), and a device can raise various alarms or errors (attribute values).…”

Section: Introductionmentioning

confidence: 99%

“…Note that a star shape is desirable as more complex structures are generally less meaningful. For instance, it is known that the influence of the friends of a friend tends to be much weaker than that of direct friends [7]. Also, patterns with complex links are hard to interpret.…”

Section: Introductionmentioning

confidence: 99%

Discovering Representative Attribute-stars via Minimum Description Length

Liu¹,

Zhou²,

Fournier‐Viger³

et al. 2022

Preprint

Self Cite

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Graphs are a popular data type found in many domains. Numerous techniques have been proposed to find interesting patterns in graphs to help understand the data and support decision-making. However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes.Graphs are a popular data type found in many domains. Numerous techniques have been proposed to find interesting patterns in graphs to help understand the data and support decision-making. However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. To address these problems, we propose a parameter-free algorithm named CSPM (Compressing Star Pattern Miner) which identifies star-shaped patterns that indicate strong correlations among attributes via the concept of conditional entropy and the minimum description length principle. Experiments performed on several benchmark datasets show that CSPM reveals insightful and interpretable patterns and is efficient in runtime. Moreover, quantitative evaluations on two real-world applications show that CSPM has broad applications as it successfully boosts the accuracy of graph attribute completion models by up to 30.68% and uncovers important patterns in telecommunication alarm data.

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Inferring social network user profiles using a partial social graph

Cited by 22 publications

References 36 publications

An Improved Louvain Algorithm for Community Detection

An Improved Louvain Algorithm for Community Detection

Semantic relatedness in DBpedia: A comparative and experimental assessment

Discovering Representative Attribute-stars via Minimum Description Length

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