The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Modeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such...
Social networking portals serve as an ideal platform for a person or an organization, to accomplish self-presentation and self-enhancement goals there by to understand their social relevance and hence, there have been many studies attempting to identify the relationship between different aspects of social media articles. Machine learning methods play a critical role in social media data analytics...
Social network is becoming indispensable of people's lives in recent years. Community detection on real network continues to be a hotspot in data ming domain. As users may join multiple social circles and interest communities, and an abundance of information can be a reflection of users' preference, heterogeneous information fusion and overlapping community detection are two key issues researchers...
The problem of node classification has been widely studied in a variety of network-based scenarios. In this paper, we will study the more challenging scenario in which some of the edges in a content-based network are labeled, and it is desirable to use this information in order to determine the labels of other arbitrary edges. Furthermore, each edge is associated with text content, which may correspond...
The structure of community represents the latent social context of user activities and has important implementations in the field of collaborative filtering in recommendation systems, in particular; when the recommended items can be inferred based on all users' interests within a community. The important issue when there are some social networks which have no ability to division due to high connectivity...
In recent years, community detection in overlapping weighted network became a research challenge. In real networks, a node can belong to two or more communities. Therefore, in this paper, we aim to address the above-mentioned problem by proposing a method to improve the modularity in overlapping weighted networks. The proposed method is based on optimizing a fitness function and fuzzy belonging degree...
Determining the frequencies and the distribution of small subgraph patterns in a large input graph is an important part of many graph based mining tasks such as Frequent Subgraph Mining (FSM) and Motif Detection. Due to the exponential number of such graph patterns the interpretation of the mining results is mostly limited to finding unexpectedly frequent patterns, and in general identifying few particularly...
In this paper, we study the problem of performing multi-label classification on networked data, where each instance in the network is assigned with multiple labels and the connections between instances are driven by various casual reasons. Networked data extracted from social media or web pages may not reflect the relationship between users in real life accurately. By mining the links that actually...
Prime intends of web mining is to mine valuable information and knowledge from web. Social network analysis has become a very well-liked field of research as it is functional for many applications. In this study we will examine the existing soft computing techniques in the area of web mining. We develop efficient methods and algorithms using soft computing approaches. Our Framework will base on Hybrid...
Social network community detection has occupied an important place in many scientific fields like biology, sociology, or computer science. This problem still attracted a lot of work. The challenge is how to identify inside these networks, groups of persons strongly linked and sharing the same preferences. As in the literature, there are many works trying to detect communities we tried in this paper...
Churn prediction is a customer relationship process that predicts for customers who are at the brink of transferring all the business to competitor. It is predicted by modeling customer behaviors in order to extract patterns. An acquaintance of a customer is more costly than retainment of an existing customer. Churn predictions shed light on members about to leave the service and support promotion...
Several challenges accompanied the growth of online social networks, such as grouping people with similar interest. Grouping like-minded people is of a high importance. Indeed, it leads to many applications like link prediction and friend or product suggestion, and explains various social phenomenon. In this paper, we present two methods of grouping like-minded people based on their textual posts...
The traditional social network community detection algorithms generally lack of consideration of link attributes, and full expression using link attribute information model and mechanism. Aiming at this issue, this paper puts forward the community detection algorithm of social network through fusion the link and node attributes. We combine similarity of node attributes between adjacent nodes and link...
Namesake is a very common phenomenon both in real world and in the Internet. This paper combines the problem of name disambiguation with clustering technique and attempts to achieve the purpose of person name disambiguation through clustering technique by putting different texts pointing to the same person to one cluster. In this paper, we propose a multi-stage strategy for text clustering. In the...
In this paper we conduct a measurement study on a campus bulletin board system (BBS). BBS users post/reply articles and form an interpersonal social network. Applying a social network analysis, we analyze the common characteristics of opinion leaders on BBS. Our results reveal the structural characteristics of this interpersonal network to be scale-free and to be of a small-world property. The existence...
Most of the existing social network systems require from their users an explicit statement of their friendship relations. In this paper we focus on implicit communities of Web users and present an approach to automatically detect such communities based on user's resource manipulations. This approach is dynamic as user groups appear and evolve along with users interests over time. Moreover, new resources...
In telecom social network, the context-based prediction analysis is an important topic, and computing of some context parameters using great amount of data is a problem so far. Architecture of DMG (data mining grid) is proposed and prototype of MDG is designed to solve the computing problem. Two of the important issues in DMG, which are the design of the workflow service in DMG and the distributed...
Nowadays the photo-capturing devices are no longer limited to digital cameras but include mobile phones, PDAs and others. This is leading to a new problem: a very large number of digital photos captured and chaotically stored in multiple locations without being annotated. This paper presents a new system, called PhotoGeo, for self-organization of georeferenced photos. The proposed system uses metadata...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.