66,175 research outputs found
Call Graph Evolution Analytics over a Version Series of an Evolving Software System
Call Graph evolution analytics can aid a software engineer when maintaining
or evolving a software system. This paper proposes Call Graph Evolution
Analytics to extract information from an evolving call graph ECG = CG_1,
CG_2,... CG_N for their version series VS = V_1, V_2, ... V_N of an evolving
software system. This is done using Call Graph Evolution Rules (CGERs) and Call
Graph Evolution Subgraphs (CGESs). Similar to association rule mining, the
CGERs are used to capture co-occurrences of dependencies in the system. Like
subgraph patterns in a call graph, the CGESs are used to capture evolution of
dependency patterns in evolving call graphs. Call graph analytics on the
evolution in these patterns can identify potentially affected dependencies (or
procedure calls) that need attention. The experiments are done on the evolving
call graphs of 10 large evolving systems to support dependency evolution
management. We also consider results from a detailed study for evolving call
graphs of Maven-Core's version series
Evolving Social Networks via Friend Recommendations
A social network grows over a period of time with the formation of new
connections and relations. In recent years we have witnessed a massive growth
of online social networks like Facebook, Twitter etc. So it has become a
problem of extreme importance to know the destiny of these networks. Thus
predicting the evolution of a social network is a question of extreme
importance. A good model for evolution of a social network can help in
understanding the properties responsible for the changes occurring in a network
structure. In this paper we propose such a model for evolution of social
networks. We model the social network as an undirected graph where nodes
represent people and edges represent the friendship between them. We define the
evolution process as a set of rules which resembles very closely to how a
social network grows in real life. We simulate the evolution process and show,
how starting from an initial network, a network evolves using this model. We
also discuss how our model can be used to model various complex social networks
other than online social networks like political networks, various
organizations etc..Comment: 5 pages, 8 figures, 2 algorithm
Exploring the Evolution of Node Neighborhoods in Dynamic Networks
Dynamic Networks are a popular way of modeling and studying the behavior of
evolving systems. However, their analysis constitutes a relatively recent
subfield of Network Science, and the number of available tools is consequently
much smaller than for static networks. In this work, we propose a method
specifically designed to take advantage of the longitudinal nature of dynamic
networks. It characterizes each individual node by studying the evolution of
its direct neighborhood, based on the assumption that the way this neighborhood
changes reflects the role and position of the node in the whole network. For
this purpose, we define the concept of \textit{neighborhood event}, which
corresponds to the various transformations such groups of nodes can undergo,
and describe an algorithm for detecting such events. We demonstrate the
interest of our method on three real-world networks: DBLP, LastFM and Enron. We
apply frequent pattern mining to extract meaningful information from temporal
sequences of neighborhood events. This results in the identification of
behavioral trends emerging in the whole network, as well as the individual
characterization of specific nodes. We also perform a cluster analysis, which
reveals that, in all three networks, one can distinguish two types of nodes
exhibiting different behaviors: a very small group of active nodes, whose
neighborhood undergo diverse and frequent events, and a very large group of
stable nodes
A System for Accessible Artificial Intelligence
While artificial intelligence (AI) has become widespread, many commercial AI
systems are not yet accessible to individual researchers nor the general public
due to the deep knowledge of the systems required to use them. We believe that
AI has matured to the point where it should be an accessible technology for
everyone. We present an ongoing project whose ultimate goal is to deliver an
open source, user-friendly AI system that is specialized for machine learning
analysis of complex data in the biomedical and health care domains. We discuss
how genetic programming can aid in this endeavor, and highlight specific
examples where genetic programming has automated machine learning analyses in
previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and
Practice 2017 worksho
Web Usage Mining with Evolutionary Extraction of Temporal Fuzzy Association Rules
In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules. (C) 2013 Elsevier B.V. All rights reserved
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