A Critical Review of Centrality Measures in Social Networks

Social networks are currently gaining increasing impact in the light of the ongoing growth of web-based services like facebook.com. One major challenge for the economically successful implementation of selected management activities such as viral marketing is the identification of key persons with an outstanding structural position within the network. For this purpose, social network analysis provides a lot of measures for quantifying a member’s interconnectedness within social networks. In this context, our paper shows the state of the art with regard to centrality measures for social networks. Due to strongly differing results with respect to the quality of different centrality measures, this paper also aims at illustrating the tremendous importance of a reflected utilization of existing centrality measures. For this purpose, the paper analyzes five centrality measures commonly discussed in literature on the basis of three simple requirements for the behavior of centrality measures

Pemetaan dan Analisis Pola Interaksi Suatu Komunitas Menggunakan Analisis Jejaring Sosial

— Jejaring sosial adalah struktur sosial yang dibentuk dari simpul-simpul yang diikat oleh satu atau lebih tipe relasi spesifik. Umumnya, hubungan kerja pada suatu komunitas atau organisasi digambarkan secara hirarkis dalam bentuk bagan. Pada kenyataannya, hubungan formal tersebut sering sekali terjadi tidak sesuai dengan kenyataannya. Pada [5], peneliti telah melakukan analisis jejaring sosial pada organisasi kepengurusan Dekanat Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) UNY berdasarkan garis komando dan koordinasi. Namun hal ini masih dirasa kurang untuk memahami pola interaksi yang sesungguhnya, sehingga penelitian ini merupakan penelitian lanjutan yang bertujuan untuk melakukan pemetaan dan analisis pola interaksi pada kepengurusan tersebut berdasar Standard of Procedure (SOP) yang ada. Berdasar SOP-SOP tersebut, dihitung ukuran-ukuran dalam jejaring sosial yang terjadi, dianalisa dan diambil kesimpulan berdasarkan properti/fitur dari graf yang terbentuk. Hasil dari penelitian ini diperoleh kesimpulan bahwa Dekan FMIPA UNY dan Ketua Jurusan Pendidikan Matematika menduduki posisi penting didalam kepengurusan Dekanat FMIPA UNY, dimana mereka merupakan information broker dari jejaring yang terbentuk. Jika dibandingkan dengan penelitian sebelumnya, [5], nilai kesetaraan dan keantaraan dari Dekan FMIPA UNY dan Ketua Jurusan Pendidikan Matematika FMIPA UNY cukup signifikan berbeda (lebih tinggi). Dari perbedaan tersebut, dapat disimpulkan bahwa berdasar SOP, pemetaan beban pekerjaan sudah cukup jelas dan merata untuk setiap bidang sedemikian sehingga, efisiensi dan efektifitas kinerja layanan di FMIPA UNY dapat disimpulkan telah mampu memberikan berbagai informasi yang dibutuhkan oleh berbagai pihak. Kata kunci: Analisis Jejaring Sosial, graf, pola interaksi, SO

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

Métricas de centralidade em redes sociais

Nos modelos de redes sociais, tal como na teoria de grafos, os vértices representam os atores e as arestas ou arcos a relação entre eles. Atores influentes são aqueles que estão frequentemente envolvidos na relação com outros atores. Este envolvimento torna-os mais visíveis sendo considerados mais centrais na rede. É neste sentido que as métricas de centralidade tentam descrever as propriedades da localização de um nó fulcral numa rede. Estas medidas têm em consideração os diferentes modos de interação e comunicação de um ator com os restantes elementos, sendo mais importantes, ou centrais, aqueles que estão localizados em posições mais estratégicas na rede. Neste trabalho apresenta-se o estudo de cinco métricas de centralidade: grau, proximidade, intermediação, vetor próprio e katz. Descrevem-se os algoritmos implementados no cálculo das medidas e apresenta-se um caso de estudo. Para completar o estudo é apresentada uma análise comparativa entre os resultados obtidos no aplicativo NodeXL, e os resultados obtidos através dos algoritmos implementados.Considering models for social networks as graphs, nodes represent the actors and the edges represent the relationship between them. Influential actors are the ones that are frequently involved on relationships between other actors. This involvement makes them more visible and considered more central on the network. In this sense centrality metrics try to describe the localization properties of an important node of the network. These measures have in consideration the different interaction and communication modes an actor has with others, being more important or central the ones that are located on more strategic locations on the network. On this work it is presented the study of five centrality measures: degree, closeness, betweenness, eigenvector and katz. It is made a description of the algorithms implemented, and it is presented a case study. To complete the study it is also made a comparative analysis between results obtained with NodeXL, and the results from the algorithms implemented

FOCUSING ON CENTRALITY MEASURE IN EMERGENCY MEDICAL SERVICES

Emergency Medical Services (EMS) attracted many researchers because the demand of EMS was increasing over time. One of the major concerns of EMS is the response time and ambulance despatching is one of the vital factors which affects the response time. This paper focuses on the problem of ambulance despatching when many emergency calls emerge in a short time, which exists under the condition of catastrophic natural or manmade disasters. We modify a new method for ambulance despatching by centrality measure, this method constructs a nearest-neighbor coupled emergency call network and then prioritize those calls by the score of fitness, where the score of fitness considers two factors: centralized measure a call by the emergency call network and the closest policy which means despatching to the closest call site. This method is testified by a series of simulation experiments on the real topology road network of Hong Kong Island which contains 8 hospitals. These analyses demonstrate the real situation and proof the potential of centrality measure in reducing response time of EMS