Improvement the Community Detection with Graph Autoencoder in Social Network Using Correlation-Based Feature Selection Method

مقدمة: في هذا البحث ، نهدف إلى تحسين طرق اكتشاف المجتمع باستخدام Graph Autoencoder. يعد اكتشاف المجتمع مرحلة حاسمة لفهم الشبكات الاجتماعية وتكوينها. طرق العمل: نقترح إطار عمل اكتشاف المجتمع باستخدام نموذج Graph Autoencoder  (CDGAE)، حيث قمنا بدمج ميزة العقد مع هيكل الشبكة كمدخل لطريقتنا. تستخدم CDGAE إستراتيجية قائمة على قياس المركزية للتعامل مع مجموعة البيانات الخالية من الميزات من خلال توفير ميزات اصطناعية لعقدها. تم تحسين أداء النموذج من خلال تطبيق تحديد الميزة على ميزات العقدة. يتمثل الابتكار الأساسي لـ CDGAE في إضافة عدد المجتمعات التي تم حسابها باستخدام Bethe Hessian Matrix في طبقة عنق الزجاجة لبنية Graph Autoencoder (GAE) ، لاستخراج المجتمعات مباشرةً دون استخدام أي خوارزميات تجميع. الاستنتاجات: وفقًا للنتائج التجريبية ، تؤدي إضافة ميزات اصطناعية إلى عقد مجموعة البيانات إلى تحسين الأداء. بالإضافة إلى ذلك ، حصلنا على نتائج افضل بكثير في اكتشاف المجتمع  باستخدام طريقة اختيار الميزة وبتعميق نموذج. أظهرت النتائج التجريبية أن نهجنا يتفوق على الخوارزميات الموجودة.Background: In this paper, we aim to improve community detection methods using Graph Autoencoder.  Community detection is a crucial stage in comprehend the purpose and composition of social networks. Materials and Methods: We propose a Community Detection framework using the Graph Autoencoder (CDGAE) model, we combined the nodes feature with the network topology as input to our method. A centrality measurement-based strategy is used by CDGAE to deal with the featureless dataset by providing artificial attributes to its nodes. The performance of the model was improved by applying feature selection to node features The basic innovation of CDGAE is that added the number of communities counted using the Bethe Hessian Matrix in the bottleneck layer of the graph autoencoder (GAE) structure, to directly extract communities without using any clustering algorithms. Results: According to experimental findings, adding artificial features to the dataset's nodes improves performance. Additionally, the outcomes in community detection were much better with the feature selection method and a deeper model. Experimental evidence has shown that our approach outperforms existing algorithms. Conclusion: In this study, we suggest a community detection framework using graph autoencoder (CDMEC). In order to take advantage of GAE's ability to combine node features with the network topology, we add node features to the featureless graph nodes using centrality measurement. By applying the feature selection to the features of the nodes, the performance of the model has improved significantly, due to the elimination of data noise. Additionally, the inclusion of the number of communities in the bottleneck layer of the GAE structure allowed us to do away with clustering algorithms, which helped decrease the complexity time. deepening the model also improved the community detection. Because social media platforms are dynamic

Deep Learning in Social Networks for Overlappering Community Detection

The collection of nodes is termed as community in any network system that are tightly associated to the other nodes. In network investigation, identifying the community structure is crucial task, particularly for exposing connections between certain nodes. For community overlapping, network discovery, there are numerous methodologies described in the literature. Numerous scholars have recently focused on network embedding and feature learning techniques for node clustering. These techniques translate the network into a representation space with fewer dimensions. In this paper, a deep neural network-based model for learning graph representation and stacked auto-encoders are given a nonlinear embedding of the original graph to learn the model. In order to extract overlapping communities, an AEOCDSN algorithm is used. The efficiency of the suggested model is examined through experiments on real-world datasets of various sizes and accepted standards. The method outperforms various well-known community detection techniques, according to empirical findings