1,636 research outputs found
Inferring monopartite projections of bipartite networks: an entropy-based approach
Bipartite networks are currently regarded as providing a major insight into
the organization of many real-world systems, unveiling the mechanisms driving
the interactions occurring between distinct groups of nodes. One of the most
important issues encountered when modeling bipartite networks is devising a way
to obtain a (monopartite) projection on the layer of interest, which preserves
as much as possible the information encoded into the original bipartite
structure. In the present paper we propose an algorithm to obtain
statistically-validated projections of bipartite networks, according to which
any two nodes sharing a statistically-significant number of neighbors are
linked. Since assessing the statistical significance of nodes similarity
requires a proper statistical benchmark, here we consider a set of four null
models, defined within the exponential random graph framework. Our algorithm
outputs a matrix of link-specific p-values, from which a validated projection
is straightforwardly obtainable, upon running a multiple hypothesis testing
procedure. Finally, we test our method on an economic network (i.e. the
countries-products World Trade Web representation) and a social network (i.e.
MovieLens, collecting the users' ratings of a list of movies). In both cases
non-trivial communities are detected: while projecting the World Trade Web on
the countries layer reveals modules of similarly-industrialized nations,
projecting it on the products layer allows communities characterized by an
increasing level of complexity to be detected; in the second case, projecting
MovieLens on the films layer allows clusters of movies whose affinity cannot be
fully accounted for by genre similarity to be individuated.Comment: 16 pages, 9 figure
Conflict Networks
Conflict parties are frequently involved into more than one conflict at a given time. In this paper the interrelated structure of conflictive relations is modeled as a conflict network where opponents are embedded in a local structure of bilateral conflicts. Conflict parties invest in specific conflict technology to attack their respective rivals and defend their own resources.We show that there exists a unique equilibrium for this conflict game and examine the relation between aggregated equilibrium investment (interpreted as conflict intensity) and underlying network characteristics. The derived results have implications for peaceful resolutions of conflicts because neglecting the fact that opponents are embedded into an interrelated conflict structure might have adverse consequences for conflict intensity.Network games, conflicts, conflict resolution
Structural Patterns of the Occupy Movement on Facebook
In this work we study a peculiar example of social organization on Facebook:
the Occupy Movement -- i.e., an international protest movement against social
and economic inequality organized online at a city level. We consider 179 US
Facebook public pages during the time period between September 2011 and
February 2013. The dataset includes 618K active users and 753K posts that
received about 5.2M likes and 1.1M comments. By labeling user according to
their interaction patterns on pages -- e.g., a user is considered to be
polarized if she has at least the 95% of her likes on a specific page -- we
find that activities are not locally coordinated by geographically close pages,
but are driven by pages linked to major US cities that act as hubs within the
various groups. Such a pattern is verified even by extracting the backbone
structure -- i.e., filtering statistically relevant weight heterogeneities --
for both the pages-reshares and the pages-common users networks
Key aspects of covert networks data collection:Problems, challenges, and opportunities
Data quality is considered to be among the greatest challenges in research on covert networks. This study identifies six aspects of network data collection, namely nodes, ties, attributes, levels, dynamics, and context. Addressing these aspects presents challenges, but also opens theoretical and methodological opportunities. Furthermore, specific issues arise in this research context, stemming from the use of secondary data and the problem of missing data. While each of the issues and challenges has some specific solution in the literature on organized crime and social networks, the main argument of this paper is to try and follow a more systematic and general solution to deal with these issues. To this end, three potentially synergistic and combinable techniques for data collection are proposed for each stage of data collection – biographies for data extraction, graph databases for data storage, and checklists for data reporting. The paper concludes with discussing the use of statistical models to analyse covert networks and the cultivation of relations within the research community and between researchers and practitioners
Undermining and Strengthening Social Networks through Network Modification
Social networks have well documented effects at the individual and aggregate
level. Consequently it is often useful to understand how an attempt to
influence a network will change its structure and consequently achieve other
goals. We develop a framework for network modification that allows for
arbitrary objective functions, types of modification (e.g. edge weight
addition, edge weight removal, node removal, and covariate value change), and
recovery mechanisms (i.e. how a network responds to interventions). The
framework outlined in this paper helps both to situate the existing work on
network interventions but also opens up many new possibilities for intervening
in networks. In particular use two case studies to highlight the potential
impact of empirically calibrating the objective function and network recovery
mechanisms as well as showing how interventions beyond node removal can be
optimised. First, we simulate an optimal removal of nodes from the Noordin
terrorist network in order to reduce the expected number of attacks (based on
empirically predicting the terrorist collaboration network from multiple types
of network ties). Second, we simulate optimally strengthening ties within
entrepreneurial ecosystems in six developing countries. In both cases we
estimate ERGM models to simulate how a network will endogenously evolve after
intervention
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