13 research outputs found
Low-rank Similarity Measure for Role Model Extraction
Computing meaningful clusters of nodes is crucial to analyze large networks.
In this paper, we present a pairwise node similarity measure that allows to
extract roles, i.e. group of nodes sharing similar flow patterns within a
network. We propose a low rank iterative scheme to approximate the similarity
measure for very large networks. Finally, we show that our low rank similarity
score successfully extracts the different roles in random graphs and that its
performances are similar to the pairwise similarity measure.Comment: 7 pages, 2 columns, 4 figures, conference paper for MTNS201
Tunable and Growing Network Generation Model with Community Structures
Recent years have seen a growing interest in the modeling and simulation of
social networks to understand several social phenomena. Two important classes
of networks, small world and scale free networks have gained a lot of research
interest. Another important characteristic of social networks is the presence
of community structures. Many social processes such as information diffusion
and disease epidemics depend on the presence of community structures making it
an important property for network generation models to be incorporated. In this
paper, we present a tunable and growing network generation model with small
world and scale free properties as well as the presence of community
structures. The major contribution of this model is that the communities thus
created satisfy three important structural properties: connectivity within each
community follows power-law, communities have high clustering coefficient and
hierarchical community structures are present in the networks generated using
the proposed model. Furthermore, the model is highly robust and capable of
producing networks with a number of different topological characteristics
varying clustering coefficient and inter-cluster edges. Our simulation results
show that the model produces small world and scale free networks along with the
presence of communities depicting real world societies and social networks.Comment: Social Computing and Its Applications, SCA 13, Karlsruhe : Germany
(2013
Robustness of Networks with Skewed Degree Distributions under Strategic Node Protection
Previous studies on the robustness of networks against intentional attacks have suggested that protecting a small fraction of important nodes in a network significantly improves its robustness. In this paper, we analyze the robustness of networks under several strategic node protection schemes. Strategic node protection schemes select a small fraction of nodes as important nodes, using a network measuresuch as node centrality, and protect the important nodes to prevent them from being removed by intentional attacks. Our simulation results indicate that (1) strategic node protection significantly improves the robustness of networks with skewed degree distributions, (2) the efficiency of strategic node protection schemes is affected by the strength of community structure of the network being protected, and (3) strategic node protection based on betweenness centrality can effectively improve the robustness of networks regardless of the strength of community structure.ADMNET 2016: The 4th International Workshop on Architecture, Design, Deployment and Management of Networks and Applications(COMPSAC 2016: The 40th IEEE Computer Society International Conference on Computers, Software & Applications)Place: Atlanta, Georgia, USADate: June 10-14, 201
Dynamics and stability of small social networks
The choices and behaviours of individuals in social systems combine in unpredictable ways to create complex, often surprising, social outcomes. The structure of these behaviours, or interactions between individuals, can be represented as a social network. These networks are not static but vary over time as connections are made and broken or change in intensity. Generally these changes are gradual, but in some cases individuals disagree and as a result "fall out" with each other, i.e. , actively end their relationship by ceasing all contact. These "fallouts" have been shown to be capable of fragmenting the social network into disconnected parts. Fragmentation can impair the functioning of social networks and it is thus important to better understand the social processes that have such consequences. In this thesis we investigate the question of how networks fragment: what mechanism drives the changes that ultimately result in fragmentation? To do so, we also aim to understand the necessary conditions for fragmentation to be possible and identify the connections that are most important for the cohesion of the network. To answer these questions, we need a model of social network dynamics that is stable enough such that fragmentation does not occur spontaneously, but is simultaneously dynamic enough to allow the system to react to perturbations (i.e. , disagreements). We present such a model and show that it is able to grow and maintain networks exhibiting the characteristic properties of social networks, and does so using local behavioural rules inspired by sociological theory. We then provide a detailed investigation of fragmentation and confirm basic intuitions on the importance of bridges for network cohesion. Furthermore, we show that this topological feature alone does not explain which points of the network are most vulnerable to fragmentation. Rather, we find that dependencies between edges are crucial for understanding subtle differences between stable and vulnerable bridges. This understandingof the vulnerability of different network components is likely to be valuable for preventing fragmentation and limiting the impact of social fallou