User communities and contents co‐ranking for user‐generated content quality evaluation in social networks

Summary On the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Web video classification with visual and contextual semantics

Afzal

Shah

Tufail

2019

“…Due to the various applications of OSN among researchers from the study the characterization of the network to that of financial applications, studying and analyzing of the social networks arise new challenging research topics in the literature [1][2][3][4]. Due to the various applications of OSN among researchers from the study the characterization of the network to that of financial applications, studying and analyzing of the social networks arise new challenging research topics in the literature [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Todays, online social networks (OSN) provide a suitable platform for sharing and broadcasting a variety of information and multimedia by users. Due to the various applications of OSN among researchers from the study the characterization of the network to that of financial applications, studying and analyzing of the social networks arise new challenging research topics in the literature [1][2][3][4]. OSNs have attracted widespread of attention in the existing network researches.…”

Section: Introductionmentioning

confidence: 99%

A new learning automata‐based sampling algorithm for social networks

Rezvanian

Meybodi

2015

Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to the whole network data in a reasonable amount of time. Therefore, network sampling arises to studying the characterization of real networks such as communication, technological, information, and social networks. In this paper, a sampling algorithm for complex social networks that is based on a new version of distributed learning automata (DLA) reported recently called extended DLA (eDLA) is proposed. For evaluation purpose, the eDLA-based sampling algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for a number of well-known sampling algorithms in terms of relative error and Kolmogorov-Smirnov test. It is shown that eDLA-based sampling algorithm outperforms the existing sampling algorithms. Experimental results further show that the eDLA-based sampling algorithm in comparison with the DLA-based sampling algorithm has a 26.93% improvement for the average of Kolmogorov-Smirnov value for degree distribution taken over all test networks. Therefore, researchers try to study and analyze real networks such as communication, technological, information and OSNs via some metrics (i.e., centrality measures) or some techniques (i.e., network sampling methods) to estimate the characterization of these networks. Besides, some hidden important properties of networks (e.g., user age distribution, user activities, user connectivity, and many more in OSN) are discovered by studying on small sampled data. It is noted that sometimes sampling methods applied on the networks subject to preserve vital properties of the networks to reach a suitable visualization.A social network can be represented as a graph with a set of nodes such as users and a certain type of relationship between users as edges of graph. Let G = hV, Ei be a given social network graph, where V is the set of nodes and E is the set of edges. Sampled network G s = hV s , E s i is the induced subgraph of G by sampling rate ϕ, where V s ⊆ V, E s ⊆ E and 0 < ϕ < 1. It is noted that the main goal of sampling algorithms is to achieve a smaller subgraph similar to the original graph G. Sampling algorithms [8-10] play an important role in preprocessing, characterizing, studying, and analyzing the real networks. Network sampling can be applied for characterization of a small portion of a network subject to preserve main properties of the original network. For example, due to privacy restrictions of some OSN services, accessibility to the whole network information is not practical, therefore, the network is studied and characterized by estimated small samples from an important part of the original network. Taking into account large size, complexity, and dynamic properties of such real networks, most classic...

“…In particular, we consider the design of a CCN based social network application since it allows us to consider the problem of rapidly connecting/disconnecting clients, such as mobile clients, and also to evaluate the mechanisms that can be leveraged to deliver asynchronous notifications to clients. This context is significant as already discussed in . We propose a design based on the idea of location‐dependent host identifiers which make each host involved in any form of dynamic push application, able to receive automatic update from a content source.…”

Section: Introductionmentioning

confidence: 99%

Push applications and dynamic content generation over content‐centric networking

Virgilio

Marchetto

Sisto

2015

SUMMARYContent-Centric Networking (CCN) represents an established candidate for the future Internet, proposing a routing architecture designed to elevate content to first class entity. Starting from the fact that the network usage has dramatically evolved towards content retrieval, CCN relies on an on-demand pull based mechanism to transfer data from the different sources to the heterogeneous consumers. This paradigm enhances the network in a number of ways, ranging from the newly introduced in-network caching capabilities to the benefits provided by the symmetric data routing adopted by CCN. In this renewed network scenario, we place our attention to those applications that do not perfectly fit the pull paradigm, stating that they need to be supported as well and proposing an effective way to achieve scalability on large scale push applications. We provide the following contributions: (i) we identify the functions that a data-centric architecture should support; (ii) propose and compare our solution with the state of the art framework designed for the specific problem of pushing data to content requesters; and (iii) evaluate their performance in terms of traffic generated and scalability achieved by simulating a real Internet Service Provider (ISP) topology and the realistic workload of a generic social network application.