119,797 research outputs found
Extraction and Analysis of Facebook Friendship Relations
Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud
However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms
Risks of Friendships on Social Networks
In this paper, we explore the risks of friends in social networks caused by
their friendship patterns, by using real life social network data and starting
from a previously defined risk model. Particularly, we observe that risks of
friendships can be mined by analyzing users' attitude towards friends of
friends. This allows us to give new insights into friendship and risk dynamics
on social networks.Comment: 10 pages, 8 figures, 3 tables. To Appear in the 2012 IEEE
International Conference on Data Mining (ICDM
Node similarity as a basic principle behind connectivity in complex networks
How are people linked in a highly connected society? Since in many networks a
power-law (scale-free) node-degree distribution can be observed, power-law
might be seen as a universal characteristics of networks. But this study of
communication in the Flickr social online network reveals that power-law
node-degree distributions are restricted to only sparsely connected networks.
More densely connected networks, by contrast, show an increasing divergence
from power-law. This work shows that this observation is consistent with the
classic idea from social sciences that similarity is the driving factor behind
communication in social networks. The strong relation between communication
strength and node similarity could be confirmed by analyzing the Flickr
network. It also is shown that node similarity as a network formation model can
reproduce the characteristics of different network densities and hence can be
used as a model for describing the topological transition from weakly to
strongly connected societies.Comment: 6 pages in Journal of Data Mining & Digital Humanities (2015)
jdmdh:3
Viral Marketing in Social Network Using Data Mining
Data mining is a process of analyzing data from different perspectives and summarizing it into useful information .The proposed work focus on clustering application which is "Collaborative Data mining "which mines different subgroups from some networks in the form of graph such as a social network. In this work using social network, two type of clusters are developed using data mining algorithms, one having strong tie and other having a weak tie between members and then the cluster with strong tie is used for discovering the highly influential node for viral advertising using target marketing to increase the profit with less advertising expense
The Complexity of Finding Effectors
The NP-hard EFFECTORS problem on directed graphs is motivated by applications
in network mining, particularly concerning the analysis of probabilistic
information-propagation processes in social networks. In the corresponding
model the arcs carry probabilities and there is a probabilistic diffusion
process activating nodes by neighboring activated nodes with probabilities as
specified by the arcs. The point is to explain a given network activation state
as well as possible by using a minimum number of "effector nodes"; these are
selected before the activation process starts.
We correct, complement, and extend previous work from the data mining
community by a more thorough computational complexity analysis of EFFECTORS,
identifying both tractable and intractable cases. To this end, we also exploit
a parameterization measuring the "degree of randomness" (the number of "really"
probabilistic arcs) which might prove useful for analyzing other probabilistic
network diffusion problems as well.Comment: 28 page
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