24 research outputs found
An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks
Many real world, complex phenomena have underlying structures of evolving
networks where nodes and links are added and removed over time. A central
scientific challenge is the description and explanation of network dynamics,
with a key test being the prediction of short and long term changes. For the
problem of short-term link prediction, existing methods attempt to determine
neighborhood metrics that correlate with the appearance of a link in the next
observation period. Recent work has suggested that the incorporation of
topological features and node attributes can improve link prediction. We
provide an approach to predicting future links by applying the Covariance
Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are
used in a linear combination of sixteen neighborhood and node similarity
indices. We examine a large dynamic social network with over nodes
(Twitter reciprocal reply networks), both as a test of our general method and
as a problem of scientific interest in itself. Our method exhibits fast
convergence and high levels of precision for the top twenty predicted links.
Based on our findings, we suggest possible factors which may be driving the
evolution of Twitter reciprocal reply networks.Comment: 17 pages, 12 figures, 4 tables, Submitted to the Journal of
Computational Scienc
A fast algorithm for predicting links to nodes of interest
The problem of link prediction has recently attracted considerable attention in various domains, such as sociology, anthropology, information science, and computer science. In many real world applications, we must predict similarity scores only between pairs of vertices in which users are interested, rather than predicting the scores of all pairs of vertices in the network. In this paper, we propose a fast similarity-based method to predict links related to nodes of interest. In the method, we first construct a sub-graph centered at the node of interest. By choosing the proper size for such a sub-graph, we can restrict the error of the estimated similarities within a given threshold. Because the similarity score is computed within a small sub-graph, the algorithm can greatly reduce computation time. The method is also extended to predict potential links in the whole network to achieve high process speed and accuracy. Experimental results on real networks demonstrate that our algorithm can obtain high accuracy results in less time than other methods can
Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs
Building trustworthy knowledge graphs for cyber-physical social systems
(CPSS) is a challenge. In particular, current approaches relying on human
experts have limited scalability, while automated approaches are often not
accountable to users resulting in knowledge graphs of questionable quality.
This paper introduces a novel pervasive knowledge graph builder that brings
together automation, experts' and crowd-sourced citizens' knowledge. The
knowledge graph grows via automated link predictions using genetic programming
that are validated by humans for improving transparency and calibrating
accuracy. The knowledge graph builder is designed for pervasive devices such as
smartphones and preserves privacy by localizing all computations. The accuracy,
practicality, and usability of the knowledge graph builder is evaluated in a
real-world social experiment that involves a smartphone implementation and a
Smart City application scenario. The proposed knowledge graph building
methodology outperforms the baseline method in terms of accuracy while
demonstrating its efficient calculations on smartphones and the feasibility of
the pervasive human supervision process in terms of high interactions
throughput. These findings promise new opportunities to crowd-source and
operate pervasive reasoning systems for cyber-physical social systems in Smart
Cities
A hybrid approach with agent-based simulation and clustering for sociograms
In the last years, some features of sociograms have proven to be strongly related to the performance of groups. However, the prediction of sociograms according to the features of individuals is still an open issue. In particular, the current approach presents a hybrid approach between agent-based simulation and clustering for simulating sociograms according to the psychological features of their members. This approach performs the clustering extracting certain types of individuals regarding their psychological characteristics, from training data. New people can then be associated with one of the types in order to run a sociogram simulation. This approach has been implemented with the tool called CLUS-SOCI (an agent-based and CLUStering tool for simulating SOCIograms). The current approach has been experienced with real data from four different secondary schools, with 38 real sociograms involving 714 students. Two thirds of these data were used for training the tool, while the remaining third was used for validating it. In the validation data, the resulting simulated sociograms were similar to the real ones in terms of cohesion, coherence of reciprocal relations and intensity, according to the binomial test with the correction of Bonferroni