LATTE: Application Oriented Social Network Embedding

In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node is represented as a feature vector. However, due to the detachment of embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community detection or information diffusion. In this paper, we propose study the application oriented heterogeneous social network embedding problem. Significantly different from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external applications in the objective function. To resolve the problem, in this paper, we propose a novel network embedding framework, namely the "appLicAtion orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network structure can be applied to compute the node "diffusive proximity" scores, which capture both local and global network structures. Based on these computed scores, Latte learns the network representation feature vectors by extending the autoencoder model model to the heterogeneous network scenario, which can also effectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous social network datasets, and the experimental results have demonstrated the outstanding performance of Latte in learning the representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl

Neural‑Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods

BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder

Network embedding aims at projecting the network data into a low-dimensional feature space, where the nodes are represented as a unique feature vector and network structure can be effectively preserved. In recent years, more and more online application service sites can be represented as massive and complex networks, which are extremely challenging for traditional machine learning algorithms to deal with. Effective embedding of the complex network data into low-dimension feature representation can both save data storage space and enable traditional machine learning algorithms applicable to handle the network data. Network embedding performance will degrade greatly if the networks are of a sparse structure, like the emerging networks with few connections. In this paper, we propose to learn the embedding representation for a target emerging network based on the broad learning setting, where the emerging network is aligned with other external mature networks at the same time. To solve the problem, a new embedding framework, namely "Deep alIgned autoencoder based eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link and attribute in a unified analytic based on broad learning, and introduces the multiple aligned attributed heterogeneous social network concept to model the network structure. A set of meta paths are introduced in the paper, which define various kinds of connections among users via the heterogeneous link and attribute information. The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network. Extensive experiments have been done on real-world aligned social networks, which have demonstrated the effectiveness of DIME in learning the emerging network embedding vectors.Comment: 10 pages, 9 figures, 4 tables. Full paper is accepted by ICDM 2017, In: Proceedings of the 2017 IEEE International Conference on Data Mining

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