Partitioning signed social networks

Doreian, Patrick; Mrvar, Andrej

doi:10.1016/j.socnet.2008.08.001

Cited by 203 publications

(167 citation statements)

References 13 publications

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“…This algorithm continuously rearranges the matrix of actors until reaching an arrangement that is closest to a predefined structure with only positive within-group ties and negative between-group ties (de Nooy et al 2005). Deviations from this ideal arrangement are indicated with an error term, which can be higher or lower depending on the number of groups defined by the researcher (Doreian and Mrvar 2009). We selected the number of groups with the lowest error term -i.e.…”

Section: Research Design and Operationalisationmentioning

confidence: 99%

Dealing with bad guys: actor- and process-level determinants of the “devil shift” in policy making

et al. 2015

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: Policy actors tend to misinterpret and distrust opponents in policy processes. This phenomenon, known as the "devil shift", consists of the following two dimensions: actors perceive opponents as more powerful and as more evil than they really are. Analysing nine policy processes in Switzerland, this article highlights the drivers of the devil shift at two levels. On the actor level, interest groups, political parties and powerful actors suffer more from the devil shift than state actors and powerless actors. On the process level, the devil shift is stronger in policy processes dealing with socio-economic issues as compared with other issues. Finally, and in line with previous studies, there is less empirical evidence of the power dimension of the devil shift phenomenon than of its evilness dimension.

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Section: Research Design and Operationalisationmentioning

confidence: 99%

Dealing with bad guys: actor- and process-level determinants of the “devil shift” in policy making

et al. 2015

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“…They use logistic regression model to predict links' values in signed networks and connected this to the balance and status theory of Davis in [11]. Doreian et al also use Davis's theory to do the partitioning of an SSN in [8]. Yang et al achieved community mining by using the links' sign values in an SSN in [1].…”

Section: Link Predictionmentioning

confidence: 99%

“…However, there are not many structure features that can be extracted from the structure of the SSN itself. Recently, researchers started to study the "meaning" of SSN structure features [8][9][10] and tried to find methods by some social psychology theories, such as structural balance theory [11]. However, the theory-based method is so strict that it cannot model the principles that drive the behaviors of social members effectively, especially the hidden principles.…”

Section: Introductionmentioning

confidence: 99%

Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

Liu

Sun

et al. 2015

Entropy

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In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations are named signed social networks (SSNs), and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members' similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN)-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services) in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets.

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“…Complex network is a method used to study the physical process or data, which is widely used in economics [14], sociology [15], medical science [16] and physics [17]. Recently, complex network has also been gradually employed to analyze the physical and mechanical properties of granular matter.…”

Section: Introductionmentioning

confidence: 99%

Effects of Inter-Particle Frictional Coefficients on Evolution of Contact Networks in Landslide Process

Lian¹,

Liu²,

Tian³

et al. 2017

ENG

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During the process of landslide, its dynamic mechanism is important to understand and predict these kinds of natural hazard. In this paper, a new method, based on concepts of complex networks, has been proposed to investigate the evolution of contact networks in mesoscale during the sliding process of slope. A slope model was established using the discrete element method (DEM), and influences of inter-particle frictional coefficients with four different values on dynamic landslides were studied. Both macroscopic analysis on slope landslide and mesoanalysis on structure evolution of contact networks, including the average degree, clustering coefficient and N-cycle, were done during the process of landslide. The analysis results demonstrate that: 1) with increasing inter-particle frictional coefficients, the displacement of slope decreases and the stable angle of slope post-failure increases, which is smaller than the peak internal frictional angle; 2) the average degree decreases with the increase of inter-particle frictional coefficient. When the displacement at the toe of the slope is smaller, the average degree there changes more greatly with increasing inter-particle frictional coefficient; 3) during the initial stage of landslide, the clustering coefficient reduces sharply, which may leads to easily slide of slope. As the landslide going on, however, the clustering coefficient increases denoting increasing stability with increasing inter-particle frictional coefficients. When the inter-particle frictional coefficient is smaller than 0.3, its variation can affect the clustering coefficient and stable inclination of slope post-failure greatly; and 4) the number of 3-cycle increases, but 4-cycle and 5-cycle decrease with increasing inter-particle frictional coefficients.

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Partitioning signed social networks

Cited by 203 publications

References 13 publications

Dealing with bad guys: actor- and process-level determinants of the “devil shift” in policy making

Dealing with bad guys: actor- and process-level determinants of the “devil shift” in policy making

Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

Effects of Inter-Particle Frictional Coefficients on Evolution of Contact Networks in Landslide Process

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