Community detection of political blogs network based on structure-attribute graph clustering model

Complex networks provide means to represent different kinds of networks with multiple features. Most biological, sensor and social networks can be represented as a graph depending on the pattern of connections among their elements. The goal of the graph clustering is to divide a large graph into many clusters based on various similarity criteria’s. Political blogs as standard social dataset network, in which it can be considered as blog-blog connection, where each node has political learning beside other attributes. The main objective of work is to introduce a graph clustering method in social network analysis. The proposed Structure-Attribute Similarity (SAS-Cluster) able to detect structures of community, based on nodes similarities. The method combines topological structure with multiple characteristics of nodes, to earn the ultimate similarity. The proposed method is evaluated using well-known evaluation measures, Density, and Entropy. Finally, the presented method was compared with the state-of-art comparative method, and the results show that the proposed method is superior to the comparative method according to the evaluations measures

A hybrid method for community detection based on user interactions, topology and frequent pattern mining

In recent years, community detection in social networks has become one of the most important research areas. One of the ways to community detection is to use interactions between users. There are different types of interactions in social networks, which, if used together with network topology, improve the precision of community identification. In this paper, a new method based on the combination of user interactions and network topology is proposed to community detection. In the community formation stage, the effective nodes are identified based on eigenvector centrality, and the primary communities around these nodes are formed based on frequent pattern mining. In the community expansion phase, small communities expand using modularity and the degree of interactions among users. To calculate the degree of interaction between users, a new measure based on the local clustering coefficient and interactions between common neighbors is proposed, which improves the accuracy of the degree of user interactions. Analysis of Higgs Twitter and Flickr datasets utilizing internal density metric, NMI and Omega demonstrates that the proposed method outperforms the other five community detection methods

Active Community Opinion Network Mining and Maximization through Social Networks Posts

Existing OM systems like CONE take a partial historical rating of users on multiple products and perform opinion estimation to maximizes overall positive opinions using OM. However, CONE does not consider actual user opinions from social posts where users provide opinions through comments, likes and sharing about a product. OBIN mines users\u27 low-frequency features from comments to create a community preference influence network utilizing user response on posts and relationships between them. However, OBIN only performs feature-level opinion mining and does not consider a joint approach that combines sentence-level and feature-level to remove subjective reviews and includes slang words and emoticons, which users often use over the internet. Also. OBIN discovers community preference but does not perform OM, which is more profitable to the seller when introducing a product in the market. The limitation of CONE and OBIN is that they consider opinion mining and maximization as separate subtasks that require more training time and do not consider community opinion, influence among the community users, nor use opinion maximization on their network to minimize viral marketing budget for selecting most influential nodes. This thesis proposes Active Community Opinion Network Mining and Maximization (ACOMax), an extension of the OBIN system that adds active OM and joint opinion mining for solving two tasks (feature and sentence opinion mining) to enhance the model\u27s accuracy by reducing training time. ACOMax first performs mining of multiple posts related to the product using TwitterAPI while considering relationships between users. Second, opinion mining (positive and neutral) from user reviews on selected posts to perform (i) Sentence-level mining to determine the overall positive sentiment of subjective opinions using VADER. (ii) feature-level opinion mining to extract frequent features with a favourable opinion about the product using the Apriori algorithm. Third, construct an opinion network graph of users who share positive opinions from (ii) to be utilized by the seller to actively select top k seed users with maximum opinion spread under Multiple Linear Threshold (MLT) for opinion maximization. To evaluate our model\u27s performance, we extracted real-time user data using TwitterAPI. Our proposed model (ACOMax) outperforms previous models for total opinion spread in terms of F1 and Accuracy with the help of joint opinion mining and solves the cold start problem of CONE, and improves the total opinion spread in a social network

Potentiale von Enterprise Social Networks und Business Process Management zur Gestaltung effizienter Unternehmen

In dieser Dissertation werden ausgewählte Fragestellungen aus den Bereichen Business Process Management (BPM) und Enterprise Social Networks (ESN) beantwortet. Im Kontext des BPM werden drei Forschungsfragen erarbeitet: (1) In welchem Ausmaß sind BPM-Maßnahmen in klein- und mittelständischen Unternehmen umgesetzt? Welche Faktoren können identifiziert werden, die sich fördernd oder hindernd auf die Umsetzung von BPM-Maßnahmen auswirken? (2) Welche Auswirkungen (z.B. Zeit, Kosten) haben BPI-Pattern in verschiedenen Szenarien? Was sind fördernde und hindernde Faktoren für die Anwendung von einzelnen BPI-Pattern? (3) Wie können kulturelle Eigenschaften anhand von Geschäftsprozessen gemessen werden und wie lassen sich die gewonnen Erkenntnisse in Prozessverbesserungsinitiativen nutzen? Im Kontext des ESN werden ebenfalls drei Forschungsfragen erarbeitet: (1) Was ist der aktuelle Stand der Literatur im Themenbereich ESN und welcher Forschungsbedarf besteht weiterhin? (2) Welche Benefits lassen sich durch den Einsatz von ESN erzielen? Können alle Benefits den traditionellen IT Capabilities zugeordnet werden oder schaffen ESN neue IT Capabilities? (3) Wie kann der Erfolg von ESN im Kontext von "Social Capital" gemessen werden und welchen Nutzen bringt ein entsprechender Messansatz? Zur Beantwortung der Forschungsfragen werden unterschiedliche Forschungsmethodiken angewendet, im Speziellen Literature Review, Survey, Case Study, Design Science, Content Analysis und Experiment. Die Forschungsergebnisse werden in wissenschaftlichen Beiträgen aufgearbeitet, wobei je Forschungsfrage ein Beitrag erarbeitet wird