34,655 research outputs found
Continuous Influence-based Community Partition for Social Networks
Community partition is of great importance in social networks because of the
rapid increasing network scale, data and applications. We consider the
community partition problem under LT model in social networks, which is a
combinatorial optimization problem that divides the social network to disjoint
communities. Our goal is to maximize the sum of influence propagation
through maximizing it within each community. As the influence propagation
function of community partition problem is supermodular under LT model, we use
the method of Lov{}sz Extension to relax the target influence
function and transfer our goal to maximize the relaxed function over a matroid
polytope. Next, we propose a continuous greedy algorithm using the properties
of the relaxed function to solve our problem, which needs to be discretized in
concrete implementation. Then, random rounding technique is used to convert the
fractional solution to integer solution. We present a theoretical analysis with
approximation ratio for the proposed algorithms. Extensive experiments
are conducted to evaluate the performance of the proposed continuous greedy
algorithms on real-world online social networks datasets and the results
demonstrate that continuous community partition method can improve influence
spread and accuracy of the community partition effectively.Comment: arXiv admin note: text overlap with arXiv:2003.1043
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
The stability of a graph partition: A dynamics-based framework for community detection
Recent years have seen a surge of interest in the analysis of complex
networks, facilitated by the availability of relational data and the
increasingly powerful computational resources that can be employed for their
analysis. Naturally, the study of real-world systems leads to highly complex
networks and a current challenge is to extract intelligible, simplified
descriptions from the network in terms of relevant subgraphs, which can provide
insight into the structure and function of the overall system.
Sparked by seminal work by Newman and Girvan, an interesting line of research
has been devoted to investigating modular community structure in networks,
revitalising the classic problem of graph partitioning.
However, modular or community structure in networks has notoriously evaded
rigorous definition. The most accepted notion of community is perhaps that of a
group of elements which exhibit a stronger level of interaction within
themselves than with the elements outside the community. This concept has
resulted in a plethora of computational methods and heuristics for community
detection. Nevertheless a firm theoretical understanding of most of these
methods, in terms of how they operate and what they are supposed to detect, is
still lacking to date.
Here, we will develop a dynamical perspective towards community detection
enabling us to define a measure named the stability of a graph partition. It
will be shown that a number of previously ad-hoc defined heuristics for
community detection can be seen as particular cases of our method providing us
with a dynamic reinterpretation of those measures. Our dynamics-based approach
thus serves as a unifying framework to gain a deeper understanding of different
aspects and problems associated with community detection and allows us to
propose new dynamically-inspired criteria for community structure.Comment: 3 figures; published as book chapte
Community detection and role identification in directed networks: understanding the Twitter network of the care.data debate
With the rise of social media as an important channel for the debate and discussion of public affairs, online social networks such as Twitter have become important platforms for public information and engagement by policy makers. To communicate effectively through Twitter, policy makers need to understand how influence and interest propagate within its network of users. In this chapter we use graph-theoretic methods to analyse the Twitter debate surrounding NHS Englands controversial care.data scheme. Directionality is a crucial feature of the Twitter social graph - information flows from the followed to the followers - but is often ignored in social network analyses; our methods are based on the behaviour of dynamic processes on the network and can be applied naturally to directed networks. We uncover robust communities of users and show that these communities reflect how information flows through the Twitter network. We are also able to classify users by their differing roles in directing the flow of information through the network. Our methods and results will be useful to policy makers who would like to use Twitter effectively as a communication medium
Interest communities and flow roles in directed networks: the Twitter network of the UK riots
Directionality is a crucial ingredient in many complex networks in which
information, energy or influence are transmitted. In such directed networks,
analysing flows (and not only the strength of connections) is crucial to reveal
important features of the network that might go undetected if the orientation
of connections is ignored. We showcase here a flow-based approach for community
detection in networks through the study of the network of the most influential
Twitter users during the 2011 riots in England. Firstly, we use directed Markov
Stability to extract descriptions of the network at different levels of
coarseness in terms of interest communities, i.e., groups of nodes within which
flows of information are contained and reinforced. Such interest communities
reveal user groupings according to location, profession, employer, and topic.
The study of flows also allows us to generate an interest distance, which
affords a personalised view of the attention in the network as viewed from the
vantage point of any given user. Secondly, we analyse the profiles of incoming
and outgoing long-range flows with a combined approach of role-based similarity
and the novel relaxed minimum spanning tree algorithm to reveal that the users
in the network can be classified into five roles. These flow roles go beyond
the standard leader/follower dichotomy and differ from classifications based on
regular/structural equivalence. We then show that the interest communities fall
into distinct informational organigrams characterised by a different mix of
user roles reflecting the quality of dialogue within them. Our generic
framework can be used to provide insight into how flows are generated,
distributed, preserved and consumed in directed networks.Comment: 32 pages, 14 figures. Supplementary Spreadsheet available from:
http://www2.imperial.ac.uk/~mbegueri/Docs/riotsCommunities.zip or
http://rsif.royalsocietypublishing.org/content/11/101/20140940/suppl/DC
Searching for network modules
When analyzing complex networks a key target is to uncover their modular
structure, which means searching for a family of modules, namely node subsets
spanning each a subnetwork more densely connected than the average. This work
proposes a novel type of objective function for graph clustering, in the form
of a multilinear polynomial whose coefficients are determined by network
topology. It may be thought of as a potential function, to be maximized, taking
its values on fuzzy clusterings or families of fuzzy subsets of nodes over
which every node distributes a unit membership. When suitably parametrized,
this potential is shown to attain its maximum when every node concentrates its
all unit membership on some module. The output thus is a partition, while the
original discrete optimization problem is turned into a continuous version
allowing to conceive alternative search strategies. The instance of the problem
being a pseudo-Boolean function assigning real-valued cluster scores to node
subsets, modularity maximization is employed to exemplify a so-called quadratic
form, in that the scores of singletons and pairs also fully determine the
scores of larger clusters, while the resulting multilinear polynomial potential
function has degree 2. After considering further quadratic instances, different
from modularity and obtained by interpreting network topology in alternative
manners, a greedy local-search strategy for the continuous framework is
analytically compared with an existing greedy agglomerative procedure for the
discrete case. Overlapping is finally discussed in terms of multiple runs, i.e.
several local searches with different initializations.Comment: 10 page
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