Empirical analysis of the user reputation and clustering property for user-object bipartite networks

2019 ◽  
Vol 30 (05) ◽  
pp. 1950035 ◽  
Author(s):  
Xiao-Lu Liu ◽  
Shu-Wei Jia ◽  
Yan Gu
Keyword(s):  
Standard Deviation ◽  
Null Model ◽  
Clustering Coefficient ◽  
Behavior Patterns ◽  
Bipartite Networks ◽  
Rating Systems ◽  
User Interest ◽  
Collective Behaviors ◽  
User Reputation ◽  
Clustering Property

User reputation is of great significance for online rating systems which can be described by user-object bipartite networks, measuring the user ability of rating accurate assessments of various objects. The clustering coefficients have been widely investigated to analyze the local structural properties of complex networks, analyzing the diversity of user interest. In this paper, we empirically analyze the relation of user reputation and clustering property for the user-object bipartite networks. Grouping by user reputation, the results for the MovieLens dataset show that both the average clustering coefficient and the standard deviation of clustering coefficient decrease with the user reputation, which are different from the results that the average clustering coefficient and the standard deviation of clustering coefficient remain stable regardless of user reputation in the null model, suggesting that the user interest tends to be multiple and the diversity of the user interests is centralized for users with high reputation. Furthermore, we divide users into seven groups according to the user degree and investigate the heterogeneity of rating behavior patterns. The results show that the relation of user reputation and clustering coefficient is obvious for small degree users and weak for large degree users, reflecting an important connection between user degree and collective rating behavior patterns. This work provides a further understanding on the intrinsic association between user collective behaviors and user reputation.

EMPIRICAL ANALYSIS OF THE CLUSTERING COEFFICIENT IN THE USER-OBJECT BIPARTITE NETWORKS

2013 ◽  
Vol 24 (08) ◽  
pp. 1350055 ◽  
Author(s):  
JIANGUO LIU ◽  
LEI HOU ◽  
YI-LU ZHANG ◽  
WEN-JUN SONG ◽  
XUE PAN
Keyword(s):  
Null Model ◽  
Clustering Coefficient ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
User Interest ◽  
Time Intervals ◽  
Data Set ◽  
Empirical Results ◽  
Maximum Value ◽  
One Year

The clustering coefficient of the bipartite network, C4, has been widely used to investigate the statistical properties of the user-object systems. In this paper, we empirically analyze the evolution patterns of C4 for a nine year MovieLens data set, where C4 is used to describe the diversity of the user interest. First, we divide the MovieLens data set into fractions according to the time intervals and calculate C4 of each fraction. The empirical results show that, the diversity of the user interest changes periodically with a round of one year, which reaches the smallest value in spring, then increases to the maximum value in autumn and begins to decrease in winter. Furthermore, a null model is proposed to compare with the empirical results, which is constructed in the following way. Each user selects each object with a turnable probability p, and the numbers of users and objects are equal to that of the real MovieLens data set. The comparison result indicates that the user activity has greatly influenced the structure of the user-object bipartite network, and users with the same degree information may have two totally different clustering coefficients. On the other hand, the same clustering coefficient also corresponds to different degrees. Therefore, we need to take the clustering coefficient into consideration together with the degree information when describing the user selection activity.

The configuration model

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Sequence ◽  
Graph Model ◽  
Network Models ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Configuration Model ◽  
Bipartite Networks ◽  
Small Component ◽  
Random Graph Model ◽  
Average Size

A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

Evaluating user reputation of online rating systems by rating statistical patterns

2021 ◽  
pp. 106895
Author(s):  
Hong-Liang Sun ◽  
Kai-Ping Liang ◽  
Hao Liao ◽  
Duan-Bing Chen
Keyword(s):  
Rating Systems ◽  
Online Rating ◽  
User Reputation

Identifying online user reputation of user–object bipartite networks

2017 ◽  
Vol 467 ◽  
pp. 508-516 ◽  
Author(s):  
Xiao-Lu Liu ◽  
Jian-Guo Liu ◽  
Kai Yang ◽  
Qiang Guo ◽  
Jing-Ti Han
Keyword(s):  
Bipartite Networks ◽  
User Reputation

ISW

2012 ◽  
Vol 4 (4) ◽  
pp. 26-40
Author(s):  
Jinlong Zeng ◽  
Guifeng Zheng
Keyword(s):  
Small World ◽  
Clustering Coefficient ◽  
P2p Network ◽  
Small World Network ◽  
Challenging Problem ◽  
P2p Networks ◽  
User Interest ◽  
Unstructured P2p Network ◽  
Simulation Results ◽  
Unstructured P2p

Content location in unstructured peer-to-peer (P2P) networks is a challenging problem. In this paper, the authors present a novel Interest-based Small World (ISW) network to address the problem, by constructing a cluster overlay in the unstructured P2P network based on the small world paradigm and user interest. There are many attractive properties of a small world network, such as low average hop distance and high clustering coefficient. Interest locality can improve the awareness of user’s indeed intentions. The authors’ scheme combines their advantage to create a better solution. The simulation results show that our scheme outperforms other schemes significantly.

scholarly journals Clustering coefficient and community structure of bipartite networks

2008 ◽  
Vol 387 (27) ◽  
pp. 6869-6875 ◽  
Author(s):  
Peng Zhang ◽  
Jinliang Wang ◽  
Xiaojia Li ◽  
Menghui Li ◽  
Zengru Di ◽  
...  
Keyword(s):  
Community Structure ◽  
Clustering Coefficient ◽  
Bipartite Networks

Mid size cliques are more common in real world networks than triangles

2014 ◽  
Vol 2 (3) ◽  
pp. 387-402 ◽  
Author(s):  
NOA SLATER ◽  
ROYI ITZCHACK ◽  
YORAM LOUZOUN
Keyword(s):  
Real World ◽  
Network Formation ◽  
Hidden Variables ◽  
Clustering Coefficient ◽  
Bipartite Networks ◽  
Connected Component ◽  
Multiple Networks ◽  
Node Connectivity ◽  
Internal Properties ◽  
Gravitation Model

AbstractReal world networks typically have large clustering coefficients. The clustering coefficient can be interpreted to be the result of a triangle closing mechanism. We have here enumerated cliques and maximal cliques in multiple networks to show that real world networks have a high number of large cliques. While triangles are more frequent than expected, large cliques are much more over-expressed, and the largest difference between real world networks and their random counterpart occurs in many networks at clique sizes of 5–7, and not at a size of 3. This does not result from the existence of few very large cliques, since a similar feature is observed when studying only maximal cliques (cliques that are not contained in other larger cliques). Moreover, when the large cliques are removed, triangles are often under-expressed.In all networks studied but one, all node members of large cliques produce a single connected component, which represent the central “core” of the network. The observed clique distribution can be explained by multiple models, mainly hidden variables model, such as the gravitation model, or the collapse of bipartite networks. These models can explain other properties of these networks, including the sub-graph distribution and the distance distribution of the networks. This suggests that node connectivity in real world networks may be determined by the similarity between the contents of the networks' nodes. This is in contrast with models of network formation that incorporate only the properties of the network, and not the internal properties of the nodes.

A topic-biased user reputation model in rating systems

2014 ◽  
Vol 44 (3) ◽  
pp. 581-607 ◽  
Author(s):  
Baichuan Li ◽  
Rong-Hua Li ◽  
Irwin King ◽  
Michael R. Lyu ◽  
Jeffrey Xu Yu
Keyword(s):  
Rating Systems ◽  
Reputation Model ◽  
User Reputation

INFORMATION FILTERING VIA CLUSTERING COEFFICIENTS OF USER–OBJECT BIPARTITE NETWORKS

2012 ◽  
Vol 23 (02) ◽  
pp. 1250012 ◽  
Author(s):  
QIANG GUO ◽  
RUI LENG ◽  
KERUI SHI ◽  
JIAN-GUO LIU
Keyword(s):  
Information Filtering ◽  
Clustering Coefficient ◽  
Data Sets ◽  
Bipartite Networks ◽  
Data Set ◽  
Algorithm Performance ◽  
User Similarity ◽  
User Clustering ◽  
Ranking Score ◽  
Clustering Coefficients

The clustering coefficient of user–object bipartite networks is presented to evaluate the overlap percentage of neighbors rating lists, which could be used to measure interest correlations among neighbor sets. The collaborative filtering (CF) information filtering algorithm evaluates a given user's interests in terms of his/her friends' opinions, which has become one of the most successful technologies for recommender systems. In this paper, different from the object clustering coefficient, users' clustering coefficients of user–object bipartite networks are introduced to improve the user similarity measurement. Numerical results for MovieLens and Netflix data sets show that users' clustering effects could enhance the algorithm performance. For MovieLens data set, the algorithmic accuracy, measured by the average ranking score, can be improved by 12.0% and the diversity could be improved by 18.2% and reach 0.649 when the recommendation list equals to 50. For Netflix data set, the accuracy could be improved by 14.5% at the optimal case and the popularity could be reduced by 13.4% comparing with the standard CF algorithm. Finally, we investigate the sparsity effect on the performance. This work indicates the user clustering coefficients is an effective factor to measure the user similarity, meanwhile statistical properties of user–object bipartite networks should be investigated to estimate users' tastes.

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