Learning and Predicting the Evolution of Social Networks

Bringmann, Björn; Berlingerio, Michele; Bonchi, Francesco; Gionis, Aristides

doi:10.1109/mis.2010.91

Cited by 127 publications

(83 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Initially, four classical proximity scores based on neighborhood were considered: PA, CN, AA and JC. These are very popular measures used in the state of the art of link prediction (Bringmann et al, 2010;Lichtenwalter et al, 2010). Each measure is explained below.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…The link prediction task was then accomplished by deploying extended versions of the proximity measures adequate for weighted networks (e.g., weighted Adamic Adar). Bringmann, Berlingerio, Bonchi, and Gionis (2010) proposed to mine network data augmented with temporal information in order to discover association rules (in terms of frequent subgraphs) that best explained the network evolution. In Juszczyszyn, Musial, and Budka (2011), the authors adopted a related approach in which the history of the network (recorded during past time windows) is used to derive probabilities of transitions between triads of nodes.…”

Section: Link Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Proximity measures for link prediction based on temporal events

Soares

Prudêncio

2013

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Baseline Methodsmentioning

confidence: 99%

Section: Link Predictionmentioning

confidence: 99%

Proximity measures for link prediction based on temporal events

Soares

Prudêncio

2013

Expert Systems with Applications

View full text Add to dashboard Cite

“…These rules are then abstracted into patterns representing the dynamics of graphs. Berlingerio et al (2009) extract patterns based on frequency and derive evolution rules to solve prediction problems in Bringmann et al (2010). All these works only focus on the graph structure and do not consider attributes related to the vertices and/or the edges.…”

Section: Related Workmentioning

confidence: 99%

“…This partially explains why graph mining has generated considerable interests in terms of both fundamental and applied research. A striking feature is its ability to allow better understanding of social interactions and to provide support for many tasks such as social recommendations (Jiang et al 2012), community discovery (Girvan and Newman 2002), social influence propagation (Goyal et al 2013), and link prediction (Bringmann et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Exceptional contextual subgraph mining

et al. 2017

View full text Add to dashboard Cite

Many relational data result from the aggregation of several individual behaviors described by some characteristics. For instance, a bike-sharing system may be modeled as a graph where vertices stand for bike-share stations and connections represent bike trips made by users from one station to another. Stations and trips are described by additional information such as the description of the geographical environment of the stations (business vs. residential area, closeness to POI, elevation, urbanization density, etc.), or properties of the bike trips (timestamp, user profile, weather, events and other special conditions about the trip). Identifying highly connected components (such as communities or quasi-cliques) in this graph provides interesting insights into global usages but does not capture mobility profiles that characterize a subpopulation. To tackle this problem we propose an approach rooted in exceptional model mining to find exceptional contextual subgraphs, i.e., subgraphs generated from a context or a description of the individual behaviors that is exceptional (behaves in a different way) compared to the whole augmented graph. The dependency between a context and an edge is assessed by a χ 2 test and the weighted relative accuracy measure is used to only retain contexts that strongly characterize connected subgraphs. We present an original algorithm that uses sophisticated pruning techniques to restrict the search space of vertices, context refinements, and edges to be considered. An experimental evaluation on synthetic data and two real-life datasets demonstrates the effectiveness of the proposed pruning mechanisms, as well as the relevance of the discovered patterns.

show abstract

“…Some works which consider dynamic networks are Bringmann et al (2010) and Bliss et al (2013). In Bringmann et al (2010), association rules and frequentpattern mining are used to search for typical patterns of structural changes in dynamic networks. The authors developed the Graph Evolution Rule Miner to extract such rules and applied these rules to predict future network evolution.…”

Section: Related Workmentioning

confidence: 99%

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

Rossetti

Guidotti

Miliou

et al. 2016

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intracommunity and inter-community link prediction.Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.

show abstract

Learning and Predicting the Evolution of Social Networks

Cited by 127 publications

References 13 publications

Proximity measures for link prediction based on temporal events

Proximity measures for link prediction based on temporal events

Exceptional contextual subgraph mining

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

Contact Info

Product

Resources

About