37 research outputs found
An Enhanced Multi-Objective Biogeography-Based Optimization Algorithm for Automatic Detection of Overlapping Communities in a Social Network with Node Attributes
Community detection is one of the most important and interesting issues in
social network analysis. In recent years, simultaneous considering of nodes'
attributes and topological structures of social networks in the process of
community detection has attracted the attentions of many scholars, and this
consideration has been recently used in some community detection methods to
increase their efficiencies and to enhance their performances in finding
meaningful and relevant communities. But the problem is that most of these
methods tend to find non-overlapping communities, while many real-world
networks include communities that often overlap to some extent. In order to
solve this problem, an evolutionary algorithm called MOBBO-OCD, which is based
on multi-objective biogeography-based optimization (BBO), is proposed in this
paper to automatically find overlapping communities in a social network with
node attributes with synchronously considering the density of connections and
the similarity of nodes' attributes in the network. In MOBBO-OCD, an extended
locus-based adjacency representation called OLAR is introduced to encode and
decode overlapping communities. Based on OLAR, a rank-based migration operator
along with a novel two-phase mutation strategy and a new double-point crossover
are used in the evolution process of MOBBO-OCD to effectively lead the
population into the evolution path. In order to assess the performance of
MOBBO-OCD, a new metric called alpha_SAEM is proposed in this paper, which is
able to evaluate the goodness of both overlapping and non-overlapping
partitions with considering the two aspects of node attributes and linkage
structure. Quantitative evaluations reveal that MOBBO-OCD achieves favorable
results which are quite superior to the results of 15 relevant community
detection algorithms in the literature
Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method
This study presents a fuzzy logical decision-making algorithm based on block theory to effectively determine discontinuous rock slope reliability under various wedge and planar slip scenarios. The algorithm was developed to provide rapid response operations without the need for extensive quantitative stability evaluations based on the rock slope sustainability ratio. The fuzzy key-block analysis method utilises a weighted rational decision (multi-criteria decision-making) function to prepare the 'degree of reliability (degree of stability-instability contingency)' for slopes as implemented through the Mathematica software package. The central and analyst core of the proposed algorithm is provided as based on discontinuity network geometrical uncertainties and hierarchical decision-making. This algorithm uses block theory principles to proceed to rock block classification, movable blocks and key-block identifications under ambiguous terms which investigates the sustainability ratio with accurate, quick and appropriate decisions especially for novice engineers in the context of discontinuous rock slope stability analysis. The method with very high precision and speed has particular matches with the existing procedures and has the potential to be utilised as a continuous decision-making system for discrete parameters and to minimise the need to apply common practises. In order to justify the algorithm, a number of discontinuous rock mass slopes were considered as examples. In addition, the SWedge, RocPlane softwares and expert assignments (25-member specialist team) were utilised for verification of the applied algorithm which led to a conclusion that the algorithm was successful in providing rational decision-making
Persian topic detection based on Human Word association and graph embedding
In this paper, we propose a framework to detect topics in social media based
on Human Word Association. Identifying topics discussed in these media has
become a critical and significant challenge. Most of the work done in this area
is in English, but much has been done in the Persian language, especially
microblogs written in Persian. Also, the existing works focused more on
exploring frequent patterns or semantic relationships and ignored the
structural methods of language. In this paper, a topic detection framework
using HWA, a method for Human Word Association, is proposed. This method uses
the concept of imitation of mental ability for word association. This method
also calculates the Associative Gravity Force that shows how words are related.
Using this parameter, a graph can be generated. The topics can be extracted by
embedding this graph and using clustering methods. This approach has been
applied to a Persian language dataset collected from Telegram. Several
experimental studies have been performed to evaluate the proposed framework's
performance. Experimental results show that this approach works better than
other topic detection methods
A Human Word Association based model for topic detection in social networks
With the widespread use of social networks, detecting the topics discussed in
these networks has become a significant challenge. The current works are mainly
based on frequent pattern mining or semantic relations, and the language
structure is not considered. The meaning of language structural methods is to
discover the relationship between words and how humans understand them.
Therefore, this paper uses the Concept of the Imitation of the Mental Ability
of Word Association to propose a topic detection framework in social networks.
This framework is based on the Human Word Association method. A special
extraction algorithm has also been designed for this purpose. The performance
of this method is evaluated on the FA-CUP dataset. It is a benchmark dataset in
the field of topic detection. The results show that the proposed method is a
good improvement compared to other methods, based on the Topic-recall and the
keyword F1 measure. Also, most of the previous works in the field of topic
detection are limited to the English language, and the Persian language,
especially microblogs written in this language, is considered a low-resource
language. Therefore, a data set of Telegram posts in the Farsi language has
been collected. Applying the proposed method to this dataset also shows that
this method works better than other topic detection methods