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

Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP

A Model to Measure the Spread Power of Rumors

Nowadays, a significant portion of daily interacted posts in social media are infected by rumors. This study investigates the problem of rumor analysis in different areas from other researches. It tackles the unaddressed problem related to calculating the Spread Power of Rumor (SPR) for the first time and seeks to examine the spread power as the function of multi-contextual features. For this purpose, the theory of Allport and Postman will be adopted. In which it claims that there are two key factors determinant to the spread power of rumors, namely importance and ambiguity. The proposed Rumor Spread Power Measurement Model (RSPMM) computes SPR by utilizing a textual-based approach, which entails contextual features to compute the spread power of the rumors in two categories: False Rumor (FR) and True Rumor (TR). Totally 51 contextual features are introduced to measure SPR and their impact on classification are investigated, then 42 features in two categories "importance" (28 features) and "ambiguity" (14 features) are selected to compute SPR. The proposed RSPMM is verified on two labelled datasets, which are collected from Twitter and Telegram. The results show that (i) the proposed new features are effective and efficient to discriminate between FRs and TRs. (ii) the proposed RSPMM approach focused only on contextual features while existing techniques are based on Structure and Content features, but RSPMM achieves considerably outstanding results (F-measure=83%). (iii) The result of T-Test shows that SPR criteria can significantly distinguish between FR and TR, besides it can be useful as a new method to verify the trueness of rumors

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

How people think, feel, and behave, primarily is a representation of their personality characteristics. By being conscious of personality characteristics of individuals whom we are dealing with or decided to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications take place there. The most prominent tool in such communications, is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text a knowledge graph which is a set of interlinked descriptions of concepts, was built through matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving more powerful representation the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph which is now a knowledgeable alternative for the input text was embedded to yield an embedding matrix. Finally, to perform personality predictions the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short term memory (LSTM) and bidirectional long short term memory (BiLSTM). The results indicated a considerable improvements in prediction accuracies in all of the suggested classifiers.Comment: This is a preprint of an article published in "Computational Intelligence and Neuroscience

Text-Based Automatic Personality Prediction Using KGrAt-Net; A Knowledge Graph Attention Network Classifier

Nowadays, a tremendous amount of human communications take place on the Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate the relationships among them. To this end, this paper aims to propose KGrAt-Net which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, first, it tries to acquire a knowingful representation of the knowledge behind the concepts in the input text through building its equivalent knowledge graph. A knowledge graph is a graph-based data model that formally represents the semantics of the existing concepts in the input text and models the knowledge behind them. Then, applying the attention mechanism, it efforts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. The results demonstrated that KGrAt-Net considerably improved the personality prediction accuracies. Furthermore, KGrAt-Net also uses the knowledge graphs' embeddings to enrich the classification, which makes it even more accurate in APP

Fuzzy clustering based on Forest optimization algorithm

AbstractClustering is one of the classification methods for data analysis and it is one of the ways of data analysis, too. There are various methods for fuzzy clustering using optimization algorithms such as genetic algorithm and particle swarm optimization algorithm that were specified. In this paper, the combination of one of the recent optimization algorithms called Forest optimization algorithm and one of the local search methods called gradient method are used to perform fuzzy clustering. The purpose of applying the gradient method is accelerating the convergence of the used optimization algorithm. To apply the proposed method, 4 types of real data sets are used. Cluster validity measures are used to obtain and verify the accuracy of the proposed method (FOFCM). By analyzing and comparing the results of the proposed method with the results of algorithms GGAFCM (fuzzy clustering based on genetic algorithm) and PSOFCM (fuzzy clustering based on particle swarm optimization algorithm), it has been shown that the accuracy of the proposed approach is significantly increased

Fuzzy clustering based on Forest optimization algorithm

Clustering is one of the classification methods for data analysis and it is one of the ways of data analysis, too. There are various methods for fuzzy clustering using optimization algorithms such as genetic algorithm and particle swarm optimization algorithm that were specified. In this paper, the combination of one of the recent optimization algorithms called Forest optimization algorithm and one of the local search methods called gradient method are used to perform fuzzy clustering. The purpose of applying the gradient method is accelerating the convergence of the used optimization algorithm. To apply the proposed method, 4 types of real data sets are used. Cluster validity measures are used to obtain and verify the accuracy of the proposed method (FOFCM). By analyzing and comparing the results of the proposed method with the results of algorithms GGAFCM (fuzzy clustering based on genetic algorithm) and PSOFCM (fuzzy clustering based on particle swarm optimization algorithm), it has been shown that the accuracy of the proposed approach is significantly increased

Unsupervised Domain Adaptation for Image Classification Using Non-Euclidean Triplet Loss

In recent years, computer vision tasks have increasingly used deep learning techniques. In some tasks, however, due to insufficient data, the model is not properly trained, leading to a decrease in generalizability. When trained on a dataset and tested on another similar dataset, the model predicts near-random results. This paper presents an unsupervised multi-source domain adaptation that improves transfer learning and increases generalizability. In the proposed method, a new module infers the source of the input data based on its extracted features. By making the features extractor compete against this objective, the learned feature representation generalizes better across the sources. As a result, representations similar to those from different sources are learned. That is, the extracted representation is generic and independent of any particular domain. In the training stage, a non-Euclidean triplet loss function is also utilized. Similar representations for samples belonging to the same class can be learned more effectively using the proposed loss function. We demonstrate how the developed framework may be applied to enhance accuracy and outperform the outcomes of already effective transfer learning methodologies. We demonstrate how the proposed strategy performs particularly well when dealing with various dataset domains or when there are insufficient data