scholarly journals Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jinyu Hu ◽  
Zhiwei Gao ◽  
Weisen Pan
Keyword(s):  
Social Network ◽  
Assessment Method ◽  
Similarity Network ◽  
Recommendation Algorithms ◽  
Simulation Results ◽  
Six Dimensions ◽  
Network Evaluation

Multiangle social network recommendation algorithms (MSN) and a new assessment method, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithm from resource point (UBR), user-based algorithm from tag point (UBT), resource-based algorithm from tag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels.

scholarly journals Dynamics of social network emergence explain network evolution

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Caleb Pomeroy ◽  
Robert M. Bond ◽  
Peter J. Mucha ◽  
Skyler J. Cranmer
Keyword(s):  
Social Network ◽  
Scientific Collaboration ◽  
Behavioral Outcomes ◽  
Network Evolution ◽  
Networked Systems ◽  
Rapid Expansion ◽  
Survey Results ◽  
Simulation Results ◽  
Initial Process ◽  
Initial Cycle

AbstractNetworked systems emerge and subsequently evolve. Although several models describe the process of network evolution, researchers know far less about the initial process of network emergence. Here, we report temporal survey results of a real-world social network starting from its point of inception. We find that individuals’ ties undergo an initial cycle of rapid expansion and contraction. This process helps to explain the eventual interactions and working structure in the network (in this case, scientific collaboration). We propose a stylized concept and model of “churn” to describe the process of network emergence and stabilization. Our empirical and simulation results suggest that these network emergence dynamics may be instrumental for explaining network details, as well as behavioral outcomes at later time periods.

scholarly journals Applicability of Feng Shui Concepts for Korean Eum-Taek Sites Verified Using Three-Dimensional Digital Mapping and Simulations

2020 ◽  
Vol 12 (21) ◽  
pp. 8904
Author(s):  
Seung-Ju Choe ◽  
Seung-Hoon Han
Keyword(s):  
Environmental Factors ◽  
Evaluation Method ◽  
Thermal Environment ◽  
Sun Exposure ◽  
Assessment Method ◽  
Living Environment ◽  
Feng Shui ◽  
Site Assessment ◽  
Simulation Results ◽  
Previous Step

The purpose of this research is to examine whether eum-taek, a feng shui theory for the dead, can be applied to Korean modern architecture. In the first step, common environmental factors that are valued in both feng shui and ecological architecture were derived, and then this research reviewed how properly the traditional site assessment method evaluated them; for example, metaphorized basic concepts of the evaluation theory based on territorial settings can be applied to evaluate common environmental factors. For the second step, this paper reviewed whether the evaluation method for feng shui presented in the previous step was applied equally between yang-taek and eum-taek theories, investigated the differences between them in general, and derived environmental factors to be utilized for evaluation in the field of architecture. As a result, it was found that the major concepts presented in the previous step have been commonly used evaluation criteria, regardless of the categories from traditional theories. The third step was to simulate whether sites selected by each theory actually have similar environmental conditions. The simulation analysis found that all analysis sites were able to obtain a higher sun exposure time than the Korean average; therefore, it was considered that their locations could have environmental advantage, in terms of solar radiation and thermal environment. The simulation results confirm that the target sites have a living environment that would be easy for humans to live in. Finally, the simulation results confirm that the eum-taek site has a living environment that is comfortable for humans to live in. If studies of the site assessment method are carried out considering yang-taek and eum-taek with different evaluation categories, the modern applicability of feng shui may increase.

scholarly journals THE SOCIAL NETWORK OF CONTEMPORARY POPULAR MUSICIANS

2007 ◽  
Vol 17 (07) ◽  
pp. 2281-2288 ◽  
Author(s):  
JUYONG PARK ◽  
OSCAR CELMA ◽  
MARKUS KOPPENBERGER ◽  
PEDRO CANO ◽  
JAVIER M. BULDÚ
Keyword(s):  
Social Networks ◽  
Social Network ◽  
The Other ◽  
Collaboration Network ◽  
Perceived Similarity ◽  
Similarity Network ◽  
Self Organized ◽  
The Social ◽  
Information Database

In this paper, we analyze two social network datasets of contemporary musicians constructed from allmusic.com (AMG), a music and artists' information database: one is the collaboration network in which two musicians are connected if they have performed or produced an album together, and the other is the similarity network in which they are connected if they were musically similar according to the music experts. We find that, while both networks exhibit typical features of social networks such as high transitivity (clustering), we find that they differ significantly in some key network features such as the degree and the betweenness distributions. We believe that this highlights the fundamental differences in the construction mechanism (self-organized collaboration and human-perceived similarity) of the new networks.

Toward Social-Semantic Recommender Systems

2016 ◽  
Vol 7 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Dalia Sulieman ◽  
Maria Malek ◽  
Hubert Kadima ◽  
Dominique Laurent
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Semantic Information ◽  
Basic Problem ◽  
Real Data ◽  
Data Sets ◽  
The Social ◽  
Recommendation Algorithms ◽  
Hybrid Recommendation

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

scholarly journals Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Shelley D. Dionne ◽  
Hiroki Sayama ◽  
Francis J. Yammarino
Keyword(s):  
Decision Making ◽  
Social Network ◽  
Network Structure ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Theoretic Approach ◽  
Agent Based ◽  
Social Network Structure ◽  
Agent Based Simulations ◽  
Simulation Results

Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theoretic approach to collective decision making, agent-based simulations were conducted to investigate how human collective decision making would be affected by the agents’ diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding. Simulation results also indicated the importance of balance between selection-oriented (i.e., exploitative) and variation-oriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing nontrivial social network structure generally improved the quality of ideas at the cost of decision convergence. Simulations with different social network topologies revealed collective decision making on small-world networks with high local clustering tended to achieve highest decision quality more often than on random or scale-free networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multilevel decision making are discussed.

Based on the BP Neural Network of Logistics Equipment Support Capability Evaluation

2012 ◽  
Vol 546-547 ◽  
pp. 1090-1094
Author(s):  
Jian Sheng Hao ◽  
Qi Zhi Huang ◽  
Shu Dong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Assessment Method ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Engineering Theory ◽  
Simulation Results ◽  
Capability Evaluation ◽  
Theory Research

In this paper, the system engineering theory research logistical equipment safeguard ability assessment method, and established the equipment support of the evaluation index system, using BP neural network can to approximate any nonlinear system advantage, based on the BP neural network of logistics equipment support capability evaluation model for logistics equipment safeguard the ability to provide a new method. The simulation results show that this method can ensure objectivity.

scholarly journals A Tag And Social Network Based Recommender System

2021 ◽  
Author(s):  
Sogol Naseri
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
User Similarity ◽  
Nearest Neighbours ◽  
The Social ◽  
Recommendation Algorithms ◽  
Internet Information ◽  
Positive Results

In the era of the Internet, information overload is a growing problem which refers to the inability of a person to make a decision because the amount of information that she/he needs to process is huge. To solve this problem, recommender systems were proposed to apply various algorithms to recognize users’ preferences and generate recommendations which are likely match the user’s interest on various items. In this thesis, we aim to improve the effectiveness of the recommendation by incorporating the social data into the traditional recommendation algorithms. Hence, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendships and memberships, in measuring the nearest neighbours. Subsequently, we define a new recommendation method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on a Last.fm dataset show positive results of our proposed approach.

scholarly journals A Tag And Social Network Based Recommender System

2021 ◽  
Author(s):  
Sogol Naseri
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
User Similarity ◽  
Nearest Neighbours ◽  
The Social ◽  
Recommendation Algorithms ◽  
Internet Information ◽  
Positive Results

In the era of the Internet, information overload is a growing problem which refers to the inability of a person to make a decision because the amount of information that she/he needs to process is huge. To solve this problem, recommender systems were proposed to apply various algorithms to recognize users’ preferences and generate recommendations which are likely match the user’s interest on various items. In this thesis, we aim to improve the effectiveness of the recommendation by incorporating the social data into the traditional recommendation algorithms. Hence, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendships and memberships, in measuring the nearest neighbours. Subsequently, we define a new recommendation method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on a Last.fm dataset show positive results of our proposed approach.

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