43 research outputs found
Hardware Parallelization of Cores Accessing Memory with Irregular Access Patterns
This project studies FPGA-based heterogeneous computing architectures with the objective of
discovering their ability to optimize the performances of algorithms characterized by irregular
memory access patterns. The example used to achieve this is a graph algorithm known as Triad
Census Algorithm, whose implementation has been developed and tested.
First of all, the triad census algorithm is presented, explaining the possible variants and
reviewing the existing implementations upon different architectures. The analysis focuses on
the parallelization techniques which have allowed to boost performance, thus reducing execution
time. Besides, the study tackles the OpenCL programming model, the standard used to develop
the final application. Special attention is paid to the language details that have motivated some
of the most important design decisions.
The dissertation continues with the description of the project implementation, including
the application objectives, the system design, and the different variants developed to enhance
algorithm performance.
Finally, some of the experimental results are presented and discussed. All implemented
versions are evaluated and compared to decide which is the best in terms of scalability and
execution time
Link Prediction Based on Subgraph Evolution in Dynamic Social Networks
We propose a new method for characterizing the dynamics of complex networks with its application to the link prediction problem. Our approach is based on the discovery of network subgraphs (in this study: triads of nodes) and measuring their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads found in the network, then we show how it can help to discover and quantify the dynamic patterns of network evolution. We also propose the application of TTM to link prediction with an algorithm (called TTM-predictor) which shows good performance, especially for sparse networks analyzed in short time scales. The future applications and research directions of our approach are also proposed and discussed
On analysis of complex network dynamics – changes in local topology
Social networks created based on data gathered in various computer systems are structures that constantly evolve. The nodes and their connections change because they are influenced by the external to the network events.. In this work we present a new approach to the description and quantification of patterns of complex dynamic social networks illustrated with the data from the Wroclaw University of Technology email dataset. We propose an approach based on discovery of local network connection patterns (in this case triads of nodes) as well as we measure and analyse their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads, after that we show how it can help to discover the dynamic patterns of network evolution. One of the main issues when investigating the dynamical process is the selection of the time window size. Thus, the goal of this paper is also to investigate how the size of time window influences the shape of TTM and how the dynamics of triad number change depending on the window size. We have shown that, however the link stability in the network is low, the dynamic network evolution pattern expressed by the TTMs is relatively stable, and thus forming a background for fine-grained classification of complex networks dynamics. Our results open also vast possibilities of link and structure prediction of dynamic networks. The future research and applications stemming from our approach are also proposed and discussed
The Dynamic Structural Patterns of Social Networks Based on Triad Transitions
In modern social networks built from the data collected in various computer systems we observe constant changes corresponding to external events or the evolution of underlying organizations. In this work we present a new approach to the description and quantifying evolutionary patterns of social networks illustrated with the data from the Enron email dataset. We propose the discovery of local network connection patterns (in this case: triads of nodes), measuring their transitions during network evolution and present the preliminary results of this approach. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads, then we show how it can help to discover the dynamic patterns of network evolution. Also, we analyse the roles performed by different triads in the network evolution by the creation of triad transition graph built from the TTM, which allows us to characterize the tendencies of structural changes in the investigated network. The future application
On the discovery of social roles in large scale social systems
The social role of a participant in a social system is a label
conceptualizing the circumstances under which she interacts within it. They may
be used as a theoretical tool that explains why and how users participate in an
online social system. Social role analysis also serves practical purposes, such
as reducing the structure of complex systems to rela- tionships among roles
rather than alters, and enabling a comparison of social systems that emerge in
similar contexts. This article presents a data-driven approach for the
discovery of social roles in large scale social systems. Motivated by an
analysis of the present art, the method discovers roles by the conditional
triad censuses of user ego-networks, which is a promising tool because they
capture the degree to which basic social forces push upon a user to interact
with others. Clusters of censuses, inferred from samples of large scale network
carefully chosen to preserve local structural prop- erties, define the social
roles. The promise of the method is demonstrated by discussing and discovering
the roles that emerge in both Facebook and Wikipedia. The article con- cludes
with a discussion of the challenges and future opportunities in the discovery
of social roles in large social systems
Measuring social dynamics in a massive multiplayer online game
Quantification of human group-behavior has so far defied an empirical,
falsifiable approach. This is due to tremendous difficulties in data
acquisition of social systems. Massive multiplayer online games (MMOG) provide
a fascinating new way of observing hundreds of thousands of simultaneously
socially interacting individuals engaged in virtual economic activities. We
have compiled a data set consisting of practically all actions of all players
over a period of three years from a MMOG played by 300,000 people. This
large-scale data set of a socio-economic unit contains all social and economic
data from a single and coherent source. Players have to generate a virtual
income through economic activities to `survive' and are typically engaged in a
multitude of social activities offered within the game. Our analysis of
high-frequency log files focuses on three types of social networks, and tests a
series of social-dynamics hypotheses. In particular we study the structure and
dynamics of friend-, enemy- and communication networks. We find striking
differences in topological structure between positive (friend) and negative
(enemy) tie networks. All networks confirm the recently observed phenomenon of
network densification. We propose two approximate social laws in communication
networks, the first expressing betweenness centrality as the inverse square of
the overlap, the second relating communication strength to the cube of the
overlap. These empirical laws provide strong quantitative evidence for the Weak
ties hypothesis of Granovetter. Further, the analysis of triad significance
profiles validates well-established assertions from social balance theory. We
find overrepresentation (underrepresentation) of complete (incomplete) triads
in networks of positive ties, and vice versa for networks of negative ties...Comment: 23 pages 19 figure
Short Cycles Connectivity
Short cycles connectivity is a generalization of ordinary connectivity.
Instead by a path (sequence of edges), two vertices have to be connected by a
sequence of short cycles, in which two adjacent cycles have at least one common
vertex. If all adjacent cycles in the sequence share at least one edge, we talk
about edge short cycles connectivity.
It is shown that the short cycles connectivity is an equivalence relation on
the set of vertices, while the edge short cycles connectivity components
determine an equivalence relation on the set of edges. Efficient algorithms for
determining equivalence classes are presented.
Short cycles connectivity can be extended to directed graphs (cyclic and
transitive connectivity). For further generalization we can also consider
connectivity by small cliques or other families of graphs