Image Recommendation Model for Social Media

In recent years, social networks based on images are the most popular social interfaces. With colossal pictures transferred regular, understanding client’s inclinations on client produced pictures and causing suggestions to have become a critical need. In fact, many composite models have been proposed to intertwine different sorts of side data like image visual representation, social networks and client-image historical behavior for developing the performance of image recommendation. However, due to the special attributes of the client produced images in social interfaces, prior studies failed to identify the complex angles that impacts the client’s preferences. In addition, the greater part of these half and half models depended on predefined loads in consolidating various types of data, which for the most part brought about problematic suggestion execution in this paper we construct a recommended model based on the hierarchy of social images. In addition to latent client intrigue demonstrating in the well-known matrix factorization-based proposal, we distinguish three key angles (i.e., Trending history, user’s appraisal and owner admiration) that influence every client's latent preferences, where every aspect summarizes a logical factor from the complex connections among clients and images. From that point forward, we structure a hierarchical attention network that normally reflects the hierarchical relationship of client’s latent interest with the distinguished key viewpoints. Finally, we identified three social contextual aspects that influence a client’s preference to an image from heterogeneous data: Trending history, user’s appraisal and relevance recommendation, we designed a hierarchical attention network to recommend images according to client preference.

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Architecture of a distributed system for processing heterogeneous data from social networks

Informatization and communication ◽

10.34219/2078-8320-2020-11-4-97-100 ◽

2020 ◽

Vol 4 ◽

pp. 97-100

Author(s):

A.P. Pronichev ◽

Keyword(s):

Social Networks ◽

Data Analysis ◽

Distributed System ◽

Heterogeneous Data ◽

Analysis Process ◽

Support Costs

The article discusses the architecture of a system for collecting and analyzing heterogeneous data from social networks. This architecture is a distributed system of subsystem modules, each of which is responsible for a separate task. The system also allows you to use external systems for data analysis, providing the necessary interface abstraction for connection. This allows for more flexible customization of the data analysis process and reduces development, implementation and support costs.

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The Relationship of State and Society: Productive or Unproductive

Journal of Developing Societies ◽

10.1177/0169796x0201800102 ◽

2002 ◽

Vol 18 (1) ◽

pp. 23-45 ◽

Cited By ~ 3

Author(s):

Richard Grabowski

Keyword(s):

Social Networks ◽

Rural Development ◽

Social Cohesion ◽

Land Reform ◽

Long Run ◽

State And Society ◽

The Creation ◽

Relationship Of ◽

The Relationship

The policies followed by patrimonial states generally involve playing one group against another and are inimical to long-run growth. Social cohesion or closure among rural groups (tenants, part-owners, etc.) provides a mechanism by which the governing elite are likely to find increased opportunities to behave in a developmental way. More strongly, this rural cohesion or closure often compels them to behave in a developmental manner. Such closure is most likely to result from broad based rural development resulting in the creation of extensive social networks via the operation of intermediaries. The prewar experiences of Japan and Korea with land reform are used to illustrate the argument.

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HINTERLAND MEDAN CITY BETWEEN BINJAI CITY: A GRAVITATION APPROACH

Jurnal Ekonomi Pembangunan ◽

10.22219/jep.v17i1.9119 ◽

2019 ◽

Vol 17 (1) ◽

pp. 1

Author(s):

Muhammad Nasir

Keyword(s):

Impulse Response Function ◽

Unit Root Test ◽

Gravity Models ◽

Causality Test ◽

Hierarchical Relationship ◽

Integration Test ◽

Variable Population ◽

Vecm Granger Causality ◽

Relationship Of ◽

The City

Regional economy explains that there is an urban hierarchical relationship, cities that have higher hierarchy will serve cities that are below it as well as cities that are in the hierarchy undersupplying cities that are in the hierarchy above them, so there is a gravitational relationship between the two. This study aims to determine the gravitational relationship of Medan city to the hinterland of the city of Binjai. Furthermore, this study also wants to explain its influence on economic growth in both cities. This analysis tools used are descriptive statistics, gravity models, unit root test, co-integration test, optimal lag, VECM, Granger causality test, impulse response function, and variance decomposition. The results showed that the city of Medan has a gravity style greater than the gravitational style of the city of Binjai. The VECM estimation results show that the gravitational variable in the city of Binjai in lag -1 and lag-2 has a positive and significant effect on the economy of Medan city. Then the economic variable of the city of Binjai itself in lag-1, the population of the city of Medan in lag-2 and the gravity of the city of Medan in lag-2 had a positive and significant effect on the economy of Binjai city. While the variable population of Binjai city in lag -1 and residents of the city of Medan in lag -1 negatively affected the economy of Binjai city.

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GroupFound: An effective approach to detect suspicious accounts in online social networks

International Journal of Distributed Sensor Networks ◽

10.1177/1550147717722499 ◽

2017 ◽

Vol 13 (7) ◽

pp. 155014771772249 ◽

Cited By ~ 2

Author(s):

Bo Feng ◽

Qiang Li ◽

Xiaowen Pan ◽

Jiahao Zhang ◽

Dong Guo

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Social Relationship ◽

False Positive Rate ◽

High Detection Rate ◽

Sina Weibo ◽

Social Communities ◽

Positive Rate ◽

Local Graph ◽

Relationship Of

Online social networks are an important part of people’s life and also become the platform where spammers use suspicious accounts to spread malicious URLs. In order to detect suspicious accounts in online social networks, researchers make a lot of efforts. Most existing works mainly utilize machine learning based on features. However, once the spammers disguise the key features, the detection method will soon fail. Besides, such methods are unable to cope with the variable and unknown features. The works based on graph mainly use the location and social relationship of spammers, and they need to build a huge social graph, which leads to much computing cost. Thus, it is necessary to propose a lightweight algorithm which is hard to be evaded. In this article, we propose a lightweight algorithm GroupFound, which focuses on the structure of the local graph. As the bi-followers come from different social communities, we divide all accounts into different groups and compute the average number of accounts for these groups. We evaluate GroupFound on Sina Weibo dataset and find an appropriate threshold to identify suspicious accounts. Experimental results have demonstrated that our algorithm can accomplish a high detection rate of [Formula: see text] at a low false positive rate of [Formula: see text].

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The relationship of social support, social networks and negative events with depression in patients with coronary artery disease

Aging & Mental Health ◽

10.1080/713649927 ◽

2000 ◽

Vol 4 (3) ◽

pp. 253-258 ◽

Cited By ~ 23

Author(s):

H. B. Bosworth ◽

D. C. Steffens ◽

M. N. Kuchibhatla ◽

W. J. Jiang ◽

R. M. Arias ◽

...

Keyword(s):

Social Support ◽

Coronary Artery Disease ◽

Social Networks ◽

Coronary Artery ◽

Negative Events ◽

Artery Disease ◽

Relationship Of ◽

The Relationship

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Community Core Evolution in Mobile Social Networks

The Scientific World JOURNAL ◽

10.1155/2013/781281 ◽

2013 ◽

Vol 2013 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Hao Xu ◽

Weidong Xiao ◽

Daquan Tang ◽

Jiuyang Tang ◽

Zhenwen Wang

Keyword(s):

Social Networks ◽

Community Structure ◽

Empirical Studies ◽

Mobile Social Networks ◽

The Real ◽

Temporary Connection ◽

Whole Process ◽

Relationship Of ◽

The Relationship ◽

Stable Community

Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks.

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Risk Analysis of Grid Operational Performance Influencing Factors Based on ISM-Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.986-987.512 ◽

2014 ◽

Vol 986-987 ◽

pp. 512-515

Author(s):

Hai Tao YUE ◽

Xiao Bao Yu ◽

Pu Yu He ◽

Hai Bo Liu ◽

Wen Yan Liu ◽

...

Keyword(s):

Risk Analysis ◽

Empirical Analysis ◽

Influencing Factors ◽

Risk Prediction ◽

Driving Force ◽

Theoretical Basis ◽

Operational Performance ◽

The Core ◽

Hierarchical Relationship ◽

Relationship Of

The ISM model is applied to the influencing factors risk analysis of grid operational performance. The research constructed multilevel hierarchical structure diagram by the association between various factors and reflected the hierarchical relationship of the index intuitively. Select a large driving force factors as the core influencing factors and develop a risk prediction radar diagram to provide a theoretical basis for grid operational performance improving. A provincial power grid company provides the data for empirical analysis to explore the applicability of the model.

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Informal Network Structure and Knowledge Sharing in Organizations: An Empirical Study of a Korean Paint Manufacturing Company

Administrative Sciences ◽

10.3390/admsci11020052 ◽

2021 ◽

Vol 11 (2) ◽

pp. 52

Author(s):

Woojin Yoon ◽

Jaeyun Jeong ◽

Kyoungwon Park

Keyword(s):

Social Networks ◽

Knowledge Sharing ◽

Network Size ◽

Weak Ties ◽

Strong Ties ◽

External Knowledge ◽

Final Sample ◽

Manufacturing Company ◽

Network Strength ◽

Relationship Of

This study investigates the potentially different roles of informal social networks in promoting knowledge sharing. Specifically, it aims to examine the effects of the focal subgroup’s between-subgroup network size and strength on knowledge sharing with other subgroups and the moderating effect of within-subgroup network strength on the relationship of between-subgroup network size to knowledge sharing. Two different online surveys were conducted to assess social networks and knowledge sharing at a paint manufacturing company located in Gyeonggi Province, South Korea. The final sample consisted of 536 employees in 58 teams. The team-level regression results showed that the focal subgroup’s between-subgroup network strength has a significant effect on knowledge sharing with other subgroups, indicating that strong ties among subgroups are more advantageous to external knowledge sharing than weak ties. The results also demonstrated that the focal subgroup’s within-subgroup network strength negatively moderates the effect of its between-subgroup network size on knowledge sharing, indicating that higher levels of between-subgroup network size are positively related to external knowledge sharing when within-subgroup network strength is weak and negatively when within-subgroup network strength is strong. The study’s findings suggest that strong ties among subgroups and weak ties among subgroup members are advantageous to external knowledge sharing.

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