53 research outputs found
Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks
Viral campaigns are crucial methods for word-of-mouth marketing in social
communities. The goal of these campaigns is to encourage people for activity.
The problem of incentivised and non-incentivised campaigns is studied in the
paper. Based on the data collected within the real social networking site both
approaches were compared. The experimental results revealed that a highly
motivated campaign not necessarily provides better results due to overlapping
effect. Additional studies have shown that the behaviour of individual
community members in the campaign based on their service profile can be
predicted but the classification accuracy may be limited.Comment: In proceedings of the 2nd International Conference on Social
Computing and its Applications, SCA 201
Influence of the Dynamic Social Network Timeframe Type and Size on the Group Evolution Discovery
New technologies allow to store vast amount of data about users interaction.
From those data the social network can be created. Additionally, because
usually also time and dates of this activities are stored, the dynamic of such
network can be analysed by splitting it into many timeframes representing the
state of the network during specific period of time. One of the most
interesting issue is group evolution over time. To track group evolution the
GED method can be used. However, choice of the timeframe type and length might
have great influence on the method results. Therefore, in this paper, the
influence of timeframe type as well as timeframe length on the GED method
results is extensively analysed.Comment: The 2012 IEEE/ACM International Conference on Advances in Social
Networks Analysis and Mining, IEEE Computer Society, 2012, pp. 678-68
Shortest Path Discovery in the Multi-layered Social Network
Multi-layered social networks consist of the fixed set of nodes linked by
multiple connections. These connections may be derived from different types of
user activities logged in the IT system. To calculate any structural measures
for multi-layered networks this multitude of relations should be coped with in
the parameterized way. Two separate algorithms for evaluation of shortest paths
in the multi-layered social network are proposed in the paper. The first one is
based on pre-processing - aggregation of multiple links into single
multi-layered edges, whereas in the second approach, many edges are processed
'on the fly' in the middle of path discovery. Experimental studies carried out
on the DBLP database converted into the multi-layered social network are
presented as well.Comment: This is an extended version of the paper ASONAM 2011, IEEE Computer
Society, pp. 497-501 DOI 10.1109/ASONAM.2011.6
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Quantifying Social Network Dynamics
The dynamic character of most social networks requires to model evolution of
networks in order to enable complex analysis of theirs dynamics. The following
paper focuses on the definition of differences between network snapshots by
means of Graph Differential Tuple. These differences enable to calculate the
diverse distance measures as well as to investigate the speed of changes. Four
separate measures are suggested in the paper with experimental study on real
social network data.Comment: In proceedings of the 4th International Conference on Computational
Aspects of Social Networks, CASoN 201
Using Machine Learning to Predict the Evolution of Physics Research
The advancement of science as outlined by Popper and Kuhn is largely
qualitative, but with bibliometric data it is possible and desirable to develop
a quantitative picture of scientific progress. Furthermore it is also important
to allocate finite resources to research topics that have growth potential, to
accelerate the process from scientific breakthroughs to technological
innovations. In this paper, we address this problem of quantitative knowledge
evolution by analysing the APS publication data set from 1981 to 2010. We build
the bibliographic coupling and co-citation networks, use the Louvain method to
detect topical clusters (TCs) in each year, measure the similarity of TCs in
consecutive years, and visualize the results as alluvial diagrams. Having the
predictive features describing a given TC and its known evolution in the next
year, we can train a machine learning model to predict future changes of TCs,
i.e., their continuing, dissolving, merging and splitting. We found the number
of papers from certain journals, the degree, closeness, and betweenness to be
the most predictive features. Additionally, betweenness increases significantly
for merging events, and decreases significantly for splitting events. Our
results represent a first step from a descriptive understanding of the Science
of Science (SciSci), towards one that is ultimately prescriptive.Comment: 24 pages, 10 figures, 4 tables, supplementary information is include
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