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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...
Friend recommendation service is a common and important demand for the users on various online platforms. Current studies mainly focus on making predictions with the neighborhood and path information derived from the personal relationship networks. However, the formed links do not indicate that two users are familiar with each other nor have intimate connections. Selective treatments are made according...
As the detection of social circles can help the users find the other users with similar interests in a big data environment to expand their friend circles, our algorithm takes the (implicit) user topic in micro-blog and the (explicit) follow relationship between the users into comprehensive account. Firstly, use the supervised-LDA model to extract user topics from micro-blogging data and calculate...
Finding communities or clusters in social networks is a famous topic in social network analysis. Most algorithms are limited to static snapshots so they cannot handle dynamics within the underlying graph. In this paper, we present a modification of the Lou-vain community detection method to handle changes in the graph without rerunning the full algorithm. Also, we adapted the Louvain greedy approach...
Community and cluster detection is a popular field of social network analysis. Most algorithms focus on static graphs or series of snapshots. In this paper we present an hierarchical algorithm, which detects communities in dynamic graphs. The method is based on the shortest paths to high-connected nodes, so called hubs. Due to local message passing, we can update the clustering results with low computational...
Semi-supervised learning makes the realistic assumptions that labelled data is typically rare, and that unlabelled data that are similar are likely to belong to the same class. Unlabelled data are assigned the labels associated with their “most similar” labelled neighbors. For graph-based semi-supervised learning, “most similar” is defined by weighted multipath path length in a graph. When classes...
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...
Link prediction is an important and well-studiedproblem in network analysis, with a broad range of applicationsincluding recommender systems, anomaly detection, and denoising. The general principle in link prediction is to use thetopological characteristics of the nodes in the network to predictedges that might be added to or removed from the network. While early research utilized local network neighborhood...
Link prediction in network attempts to predict the exist-yet-unknown links or future links in accordance with the node properties and the network typology. It has been used in many domains such as social network, biology experiment, and criminal investigations. Classical methods are based on graph topology structure and path features but few consider clustering information. Actually, clustering information...
Link prediction is a key task to identify the future links among existing non-connected members of a network, by measuring the proximity between nodes in a network. Node neighbourhood based link prediction techniques are immensely used for prediction of future links. These techniques can be applied on various applications like biological protein- protein interaction network, social network, information...
The ongoing issue in social network is detecting the communities for large data sets efficiently, stabilizing the communities in the network so that we get same structure over different runs for same network data set pose a challenging problem in the research community. Although, there were various attempts in the past to come out with an efficient and cost effective algorithm that can perform an...
With the extensive use of sensor-embedded smart phones, Location-Based Social Networks (LBSN) become more and more popular among online social networks in recent years. In social networks, constructing the shortest path with minimum cost between any two nodes efficiently is vital for both graph analysis and implementation of applications. This is well known as the routing problem in social networks...
Recommender systems (RS) are found in many online applications where users are exposed to huge sets of items. The goal of recommender systems is to provide the users with a list of recommended items that they prefer, or predict how much they might prefer each item. Collaborative Filtering (CF) is a commonly used technique in RS. This approach recommends user based on the preferences of other similar...
Community structure is one of non-trivial topological properties ubiquitously demonstrated in real-world complex networks. Related theories and approaches are of fundamental importance for understanding the functions of networks. Previously, we have proposed a probabilistic algorithm called the NCMA to efficiently as well as effectively mine communities from real-world networks. Here, we show that...
Computing of some parameters using great amount of data is a great challenge in data mining fields so far. DMG (data mining grid) is designed to solve the computing problem. The design of the workflow service in DMG and the distributed data mining algorithm, are investigated. A sample application in telecom field customer value analysis is and illustrated. In the sample application, Clique, which...
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