A supervised approach for intra-/inter-community interaction prediction in dynamic social networks

Rossetti, Giulio; Guidotti, Riccardo; Miliou, Ioanna; Pedreschi, Dino; Giannotti, Fosca

doi:10.1007/s13278-016-0397-y

Cited by 19 publications

(8 citation statements)

References 38 publications

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“…The structure of social networks is rapidly evolving. To leverage this evolving structure, [38] proposed a method that exploited characteristics calculated by a known time prediction of measures computed using a pair of nodes. The method was tested using real data.…”

Section: Link Prediction In Dynamic Snsmentioning

confidence: 99%

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

Mohameds¹,

Zakis²,

Marjans³

2019

Adv. sci. technol. eng. syst. j.

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In more recent times, researchers have turned their attention to link prediction and the role link inference can play in better understanding the evolutionary nature of social networking sites. The objective of this paper is to present an in-depth review, analysis, and discussion of the cutting-edge link prediction methods that can be applied to better understand the development of social networks. The findings of the literature review reveal that there has been a steady increase in the number of published articles that present novel link prediction models that are designed to enhance the efficiency and accuracy of link prediction. In this paper, this most recent techniques to be proposed in this regard are compared and categorized, and features and evaluation metrics are presented for each approach. The results of the evaluation reveal that there are no complete or definitive methods available that can accurately and reliably be applied within different dynamic social networks to predict missing, emerging, and broken links within the network. The paper concludes by presenting potential future directions and recommendations for further studies

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Section: Link Prediction In Dynamic Snsmentioning

confidence: 99%

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

Mohameds¹,

Zakis²,

Marjans³

2019

Adv. sci. technol. eng. syst. j.

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“…In Nicosia et al (2013) graph metrics are revisited for temporal networks in order to take into account the effects of time ordering on causality. There are works addressing community detection in evolving networks (Rossetti et al 2016;). Instead of focusing on local nodes, the literature is concentrated on the evolving patterns of groups of users in the network.…”

Section: Temporal Social Networkmentioning

confidence: 99%

On analyzing user preference dynamics with temporal social networks

et al. 2018

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The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks in information retrieval and recommendation systems domains. However, existing models are too constrained for capturing the complexity of the underlying phenomenon. Online social networks contain rich information about social interactions and relations. Thus, these become an essential source of knowledge for the understanding of user preferences evolution. In this work, we investigate the interplay between user preferences and social networks over time. First, we propose a temporal preference model able to detect preference change events of a given user. Following this, we use temporal networks concepts to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Finally, we look for a correlation between preference change events and node centrality change events over Twitter and Jam social music datasets. Our findings show that there is a strong correlation between both change events, specially when modeling social interactions by means of a temporal network. Example 3 As example, let us suppose that J ohn posts 4 times about corruption (c), 3 times about sports (s), 2 times about politics ( p) and 1 time about international (i) on time 3. The temporal preferences of John on 3 are: J ohn 3 = {c J ohn 3 s, s J ohn 3 p, p J ohn 3 i, i J ohn 3 securit y, i J ohn 3 education, i J ohn 3 entertainment, i J ohn 3 economy, i J ohn 3

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“…Weighted AUPR Positive Class AUPR 3) Time Series Forecasting: This approach combines dynamic SNA (i.e., observations of the at different time periods) with topological features to learn a robust ML model able to predict new links [8]. It also relies on communities as closely-related nodes of the graph.…”

Section: Subscriber Db Health Watchermentioning

confidence: 99%

Can Network Analysis Techniques help to Predict Design Dependencies? An Initial Study

Díaz-Pace,

Tommasel,

Godoy

2018

Preprint

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The degree of dependencies among the modules of a software system is a key attribute to characterize its design structure and its ability to evolve over time. Several design problems are often correlated with undesired dependencies among modules. Being able to anticipate those problems is important for developers, so they can plan early for maintenance and refactoring efforts. However, existing tools are limited to detecting undesired dependencies once they appeared in the system. In this work, we investigate whether module dependencies can be predicted (before they actually appear). Since the module structure can be regarded as a network, i.e, a dependency graph, we leverage on network features to analyze the dynamics of such a structure. In particular, we apply link prediction techniques for this task. We conducted an evaluation on two Java projects across several versions, using link prediction and machine learning techniques, and assessed their performance for identifying new dependencies from a project version to the next one. The results, although preliminary, show that the link prediction approach is feasible for package dependencies. Also, this work opens opportunities for further development of software-specific strategies for dependency prediction.

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A supervised approach for intra-/inter-community interaction prediction in dynamic social networks

Cited by 19 publications

References 38 publications

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

On analyzing user preference dynamics with temporal social networks

Can Network Analysis Techniques help to Predict Design Dependencies? An Initial Study

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