Community detection in multiplex networks using locally adaptive random walks

Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers. In this article we propose a community detection algorithm, LART (Locally Adaptive Random Transitions), for the detection of communities that are shared by either some or all the layers in the multiplex. The algorithm is based on a random walk on the multiplex, and the transition probabilities defining the random walk are allowed to depend on the local topological similarity between layers at any given node so as to facilitate the exploration of communities across layers. Based on this random walk, a node dissimilarity measure is derived and nodes are clustered based on this distance in a hierarchical fashion. We present experimental results using networks simulated under various scenarios to showcase the performance of LART in comparison to related community detection algorithms

Walls of glass. Measuring deprivation in social participation

Altres ajuts: RI441/6-1This paper proposes a measure for deprivation in social participation, an important but so far neglected dimension of human well-being. Operationalisation and empirical implementation of the measure are conceptually guided by the capability approach. Essentially, the paper argues that deprivation in social participation can be convincingly established by drawing on extensive non-participation in customary social activities. In doing so, the present paper synthesizes philosophical considerations, axiomatic research on poverty and deprivation, and previous empirical research on social exclusion and subjective well-being. An application using high-quality German survey data supports the measure's validity. Specifically, the results suggest, as theoretically expected, that the proposed measure is systematically different from related concepts like material deprivation and income poverty. Moreover, regression techniques reveal deprivation in social participation to reduce life satisfaction substantially, quantitatively similar to unemployment. Finally, the validity of the measure and the question of preference vs. deprivation are discussed

Perceived Software Platform Openness: The Scale and its Impact on Developer Satisfaction

Application developers are of growing importance to ensure that software platforms (e.g. Facebook, Android) gain or maintain a competitive edge. However, despite calls for research to investigate developers’ perspective on platform-centric ecosystems, no research study has been dedicated to identifying the facets that constitute developers’ perception of platform openness. In this paper, we develop a scale of platform openness as perceived by third-party application developers. Using both qualitative and quantitative methods, we conceptualize perceived platform openness as a second-order construct. Empirical evidence from a survey of Android application developers (N=254) support this construct’s validity. Furthermore, we identify perceived platform openness as a major driver of complementors’ overall satisfaction with the platform. Our study thus contributes to a better understanding of platform openness in particular and the management of platform-centric ecosystems in general

Hypergraphx: a library for higher-order network analysis

From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where hyperedges encode interactions among an arbitrary number of units. Here, we present an open-source python library, hypergraphx (HGX), providing a comprehensive collection of algorithms and functions for the analysis of higher-order networks. These include different ways to convert data across distinct higher-order representations, a large variety of measures of higher-order organization at the local and the mesoscale, statistical filters to sparsify higher-order data, a wide array of static and dynamic generative models, and an implementation of different dynamical processes with higher-order interactions. Our computational framework is general, and allows to analyse hypergraphs with weighted, directed, signed, temporal and multiplex group interactions. We provide visual insights on higher-order data through a variety of different visualization tools. We accompany our code with an extended higher-order data repository, and demonstrate the ability of HGX to analyse real-world systems through a systematic analysis of a social network with higher-order interactions. The library is conceived as an evolving, community-based effort, which will further extend its functionalities over the years. Our software is available at https://github.com/HGX-Team/hypergraph