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نتیجه جستجو - Community detection

تعداد مقالات یافته شده: 16
ردیف عنوان نوع
1 Multi-objective optimization model in a heterogeneous weighted network through key nodes identification in overlapping communities
مدل بهینه سازی چند هدفه در یک شبکه وزن دار ناهمگن از طریق شناسایی گره های کلیدی در جوامع همپوشانی -2020
Nowadays, it is possible to easily utilize positive and negative effects of neighbors on a social network to maximize diffusion of a novel product and profit of the seller. Hence, this paper aims to introduce a new mathematical model for a product pricing in non-competitive environment having multiple goals. The proposed model is designed while there are a monopole seller and several heterogeneous customers for a novel product. Considering various criteria, these customers are able to purchase the novel product including price, product quality, urgent need to have the product, and positive/negative externalities received from the neighbors. Moreover, they are able to comment in case of satisfaction or dissatisfaction with the product. However, the extent of influence depends on strength of the relations with neighbors that is considered in the proposed model with complete information and quantitative values. Proportionate to activating the neighbors, referral bonus is considered from the seller. To find influential nodes for the influence and exploit strategy implementation we propose a new overlapping community detection algorithm. In this algorithm, a new overlapping score based on non-member neighbor nodes connectivity is introduced to identify overlapping communities. Finally, we evaluate the efficiency of the proposed model, by implementing the proposed community detection algorithm in a real-world dataset. The results show that it is possible to obtain desired selling price in a fashion that maximum diffusion in the network happens and the seller achieves his desired profit under various management viewpoints.
Keywords: Diffusion | Weighted network | Non-competitive market | Monopoly pricing | Heterogeneous network | Overlapping community detection
مقاله انگلیسی
2 Graph Deconvolutional Networks
شبکه Deconvolutional گراف-2020
Graphs and networks are very common data structure for modelling complex systems that are composed of a number of nodes and topologies, such as social networks, citation networks, biological protein-protein interactions networks, etc. In recent years, machine learning has become an efficient technique to obtain representation of graph for downstream graph analysis tasks, including node classification, link prediction, and community detection. Different with traditional graph analytical models, the representation learning on graph tries to learn low dimensional embeddings by means of machine learning models that could be trained in supervised, unsupervised or semi-supervised manners. Compared with traditional approaches that directly use input node attributes, these embeddings are much more informative and helpful for graph analysis. There are a number of developed models in this respect, that are different in the ways of measuring similarity of vertexes in both original space and feature space. In order to learn more efficient node representation with better generalization property, we propose a task-independent graph representation model, called as graph deconvolutional network (GDN), and corresponding unsupervised learning algorithm in this paper. Different with graph convolution network (GCN) from the scratch, which produces embeddings by convolving input attribute vec- tors with learned filters, the embeddings of the proposed GDN model are desired to be convolved with filters so that reconstruct the input node attribute vectors as far as possible. The embeddings and filters are alternatively optimized in the learning procedure. The correctness of the proposed GDN model is verified by multiple tasks over several datasets. The experimental results show that the GDN model outperforms existing alternatives with a big margin
Keywords: graph representation | representation learning | unsupervised learning |node embedding | machine learning
مقاله انگلیسی
3 Do Chinese hospital services constitute an oligopoly? Evidence of the rich-club phenomenon in a patient referral network
آیا خدمات بیمارستان چینی یک الیگپولی است؟ شواهدی از پدیده باشگاه ثروتمند در یک شبکه مراجعه کننده به بیمار-2020
Research on medical practice that uses big data has attracted considerable attention recently. In this paper, we focused on a large set of patient referral data gathered in Fujian province, China, between 2009 and 2011. We built a directed weighted patient referral network. By using four metrics from network science, namely, the power-law distribution, global rich-club coefficient, local richclub coefficient, and assortativity coefficient, we identified a significant rich-club phenomenon in this network. In addition, the community detection was also carried out to find the relationship between rich members and non-rich members. The findings indicate an oligopoly in which Class-III hospitals occupy an overwhelmingly dominant position over the competition. Also, the characteristic ‘significant regional clustering’ was inferred from the results.
Keywords: Healthcare system | Social network analysis | Hospital | China
مقاله انگلیسی
4 A balanced modularity maximization link prediction model in social networks
مدل پیش بینی پیوند حداکثر سازگاری مدولار در شبکه های اجتماعی-2017
Link prediction has been becoming an important research topic due to the rapid growth of social networks. Community-based link prediction methods are proposed to incorporate community information in order to achieve accurate prediction. However, the performance of such methods is sensitive to the selection of community detection algorithms, and they also fail to capture the correlation between link formulation and community evolution. In this paper we introduce a balanced Modularity-Maximization Link Prediction (MMLP) model to address this issue. The idea of MMLP is to integrate the formulation of two types of links into a partitioned network generative model. We proposed a probabilistic algo rithm to emphasize the role of innerLinks, which correspondingly maximizes the network modularity. Then, a trade-off technique is designed to maintain the network in a stable state of equilibrium. We also present an effective feature aggregation method by exploring two variations of network features. Our proposed method can overcome the limit of sev eral community-based methods and the extensive experimental results on both synthetic and real-world benchmark data demonstrate its effectiveness and robustness.
Keywords: Link prediction | Social network | Community detection | Modularity
مقاله انگلیسی
5 Detecting communities in social networks using label propagation with information entropy
تشخیص جوامع در شبکه های اجتماعی با استفاده از انتشار برچسب با اطلاعات آنتروپی-2017
Community detection has become an important and effective methodology to understand the structure and function of real world networks. The label propagation algorithm (LPA) is a near-linear time algorithm used to detect non-overlapping community. However, it merely considers the direct neighbor relationship. In this paper, we propose an algorithm to consider information entropy as the measurement of the relationship between direct neighbors and indirect neighbors. In a label update, we proposed a new belonging coefficient to describe the weight of the label. With the belonging coefficient no less than a threshold each node can keep one or more labels to constitute an overlapping community. Experimental results on both real-world and benchmark networks show that our algorithm also possesses high accuracy on detecting community structure in networks.
Keywords: Community detection | Label propagation | Information entropy
مقاله انگلیسی
6 Complex network measurement and optimization of Chinese domestic movies with internet of things technology
بهینه سازی و اندازه گیری شبکه های پیچیده فیلم های داخلی چینی با فناوری اینترنت اشیاء -2017
The market performance prediction of domestic motion picture is an important problem that is worthy of study. In this paper, by incorporating Chinese fine-grained semantic fea tures, we propose a method of community detection and genetic optimization especially for Chinese domestic films. These semantic features, also named as gene elements, are used as nodes to construct a movie complex network. Through leveraging the influence of the node both in the whole network and in the internal community, four unique commu nities are revealed for successful Chinese movies. Then the Genetic Algorithm (GA) with a proposed novel fitness function is used to obtain the optimal cluster of gene elements. For the other operations in GA (i.e. initialization, selection, crossover and mutation), the pa rameters are also be optimized by a distinctive evaluation method. Finally, the experiments on the data of Chinese motion pictures in 2016 demonstrate the efficacy and accuracy of the overall system.
Keywords: Semantic feature | Movie complex network | Community detection | Genetic algorithm
مقاله انگلیسی
7 استفاده از الگوریتم الگوی تکرار شونده، برای تشخیص جوامع در شبکه‌های اجتماعی
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 28 - تعداد صفحات فایل doc فارسی: 50
اخیراٌ، در وب سایت‌های شبکه‌ی اجتماعی شاهد حجمی وسیعی از داده‌های متنوع هستیم. تحلیل یک چنین داده‌هایی منجر به کشف اطلاعات و روابط ناشناخته در این شبکه‌ها گردیده است. شناسایی جوامع، فرآیندی است که به شناسایی گره‌های مشابه می‌پردازد و لذا می‌توان آنرا وظیفه ای چالش بر انگیز در حیطه‌ی تحلیل داده‌های شبکه‌های اجتماعی دانست. این علم به طور گسترده در جامعه‌ی شبکه‌های اجتماعی و آنهم از نظر ساختارهای گراف موجود در این شبکه‌ها مورد مطالعه قرار گرفته است. شبکه‌های اجتماعی آنلاین و همچنین ساختارهای گراف، شامل اطلاعات کاربردی مفیدی در داخل شبکه‌ها می‌باشند. استفاده از این اطلاعات می‌تواند بهبود فرآیند کشف یک جامعه را به همراه داشته باشد. در این مطالعه، روشی را برای کشف یک جامعه ارائه می‌دهیم. علاوه بر استفاده از ارتباطات بین گره‌ها به منظور بهبود کیفیت جوامع کشف شده، اطلاعات محتوا را نیز مورد استفاده قرار می‌دهیم. این روش را می‌توان روشی جدید بر مبنای الگوهای تکرار شونده و فعالیت‌های کاربران در شبکه و مخصوصاٌ سایت‌های شبکه‌های اجتماعی ای دانست که کاربران یک سری فعالیت سلیقه ای را انجام می‌دهند. روش پیشنهادی ما دو نقش را ایفا می‌سازد. در ابتدا بر مبنای فعالیت‌های کاربران در شبکه، بعضی از جوامعی که دارای کاربران مشابهی می‌باشند را کشف می‌کند و به دنبال آن از روابط اجتماعی استفاده کرده و جوامع بیشتری را کشف می‌سازد. از مقیاس اف ، به منظور ارزیابی نتایج دو مجموعه‌ی داده ای واقعی استفاده می‌کنیم (Blogcatalog /Flicker). اثبات خواهیم نمود که روش پیشنهادی می‌تواند کیفیت کشف جوامع را بهبود دهد.
واژگان کلیدی: شبکه‌های اجتماعی | تشخیص جامعه | کاوش الگوی تکرار شونده | داده کاوی | تحلیل کلان داده‌ها
مقاله ترجمه شده
8 تشخیص جامعه سازگار در داده‌های شبکه چند لایه
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 20
داده های دارای چند لایه ی شبکه را در نظر بگیرید که روابط میان هر جفت عنصر در چند حالت منعکس شده و به وسیله بردارهای چند متغیره یا حتی ابعادی بالا توصیف شود. در چارچوب مدل بلوک تصادفی چند لایه، نتایج را با استفاده از حداقل ثبات مربعات بدست می آوریم. قضایای ما نشان می‌دهند که، در مقایسه با تشخیص جامعه تک - لایه، یک شبکه چند لایه اطلاعاتی بسیار غنی‌تر ارائه می کند که تشخیص جامعه سازگار را از شبکه بسیار تنک ممکن ساخته و تراکم حاشیه ای مورد نیاز به عنوان عاملی از ریشه دوم تعداد لایه‌ها کاهش می‌یابد. . علاوه بر این چارچوب این لایه ها به صورتی است که ساختار منسجمی را در بین لایه ها تشخیص داده به صورتی که مرز میان این لایه ها و تشخیص آن کار دشواری باشد. در ادامه و در قسمت نتایج نظری پژوهش به ارائه مثالی برای شبیه سازی داده ها پرداخته می شود.
برخی از کلمات کلیدی: تشخیص جامعه | ثبات | شبکه تنک | مرز طیفی تانسور
مقاله ترجمه شده
9 Community detection in social networks with node attributes based on multi-objective biogeography based optimization
تشخیص جامعه در شبکه های اجتماعی با ویژگی های گره بر اساس بهینه سازی جغرافیای زیستی چند هدفه-2017
Detecting communities in complex networks is one of the most important issues considered when analyzing these kinds of networks. A majority of studies in the field of community detection tend to detect communities through analyzing linkages of the networks. What this paper aims to achieve is to reach to a trade-off between similarity of nodes attributes and density of connections in finding communities of social networks with node attributes. Since the community detection problem can be modeled as a seriously non-linear discrete optimization problem, we have hereby proposed a multi-objective discrete Biogeography Based Optimization (BBO) algorithm to find communities in social networks with node attributes. This algorithm uses the Pareto based approach for community detection. Also, we introduced a new metric, SimAtt, to measure the similarity of node attributes in a community of a network and used it along with Modularity, which considers the linkage structure of a network to detect communities, as the two objective functions of the proposed method to be maximized. In the proposed method, a two phase sorting strategy is introduced which uses the non-dominated sorting and Crowding-distance to sort the generated solution of a population in each iteration. Moreover, this paper introduces a method for mutation probability approximation and uses a chaotic mechanism to dynamically tune the mutation probability in each iteration. Additionally, two novel strategies are introduced for mutation in unweighted and weighted networks. Since the final output of the proposed method is a set of non-dominated (Pareto-optimal) solutions, a metric named alpha_SAM is proposed to determine the best compromise solution among these non-dominated ones. Quantitative evaluations based on extensive experi ments on 14 real-life data sets reveals that the method presented in this study achieves favorable results which are quite superior to other relevant algorithms in the literature.
Keywords: Community detection | Node attributes | Discrete optimization | Biogeography based optimization | Multi-objective optimization | Pareto-based approach
مقاله انگلیسی
10 A social cognitive heuristic for adaptive data dissemination in mobile Opportunistic Networks
اکتشافی شناختی اجتماعی برای انتشار اطلاعات سازگار در شبکه های اپورتونیستی سیار-2017
It is commonly agreed that data (and data-centric services) will be one of the cornerstones of Future Internet systems. In this context, mobile Opportunistic Networks (OppNets) are one of the key paradigms to efficiently support, in a self-organising and decentralised manner, the growth of data generated by localized interactions between users mobile devices, and between them and nearby smart devices such as IoT nodes. In OppNets scenarios, the spontaneous collaboration among mobile devices is exploited to disseminate data toward interested users. However, the limited resources and knowledge available at each node, and the vast amount of data available in the network, make it difficult to devise efficient schemes to accomplish this task. Recent solutions propose to equip each device with data filtering methods derived from human information processing schemes, known as Cognitive Heuristics. They are very effective methods used by human brains to quickly drop useless information and keep only the most relevant information. Althought cognitive-based OppNet solutions proved to be efficient (with limited overheads), they can become less effective when facing dynamic scenarios or situations where nodes cannot fully collaborate with each other, as we show in this paper. One of the reasons is that the solutions proposed so far do not take take into account the social structure of the environment where the nodes are moving in. In order to be more effective, the selection of information performed by each node should take into consideration not only the relevance of content for the local device, but also for other devices will encounter in the future due to mobility. To this end, in this paper we propose a social-based data dissemination scheme, based on a cognitive heuristic, known as the Social Circle Heuristic. This heuristic is an evaluation method that exploits the structure of the social environment to make inferences about the relevance of discovered information. We show how the Social Circle Heuristic, coupled with a cognitive-based community detection scheme, can be exploited to design an effective data dissemination algorithm for OppNets. We provide a detailed analysis of the performance of the proposed solution via simulation.
Keywords: Opportunistic Networks | Cognitive Heuristics | Data Dissemination | Social | Self-Organising
مقاله انگلیسی
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