Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conduct...Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-interests(POIs) on the actual output of recommender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model(GSBPR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.展开更多
Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not appli- cable to a heterogeneous network. Registered patent history data denotes the innovations and the achieve...Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not appli- cable to a heterogeneous network. Registered patent history data denotes the innovations and the achievements in different research fields. In this paper, we present an iteration algorithm called inventor-ranking, to sort the influences of patent inventors in heterogeneous networks constructed based on their patent data. This approach is a flexible rule-based method, making full use of the features of network topology. We sort the inventors and patents by a set of rules, and the algorithm iterates continuously until it meets a certain convergence condition. We also give a detailed analysis of influential inventor＇s interesting topics using a latent Dirichlet allocation （LDA） model. Compared with the traditional methods such as PageRank, our approach takes full advantage of the information in the heterogeneous network, including the relationship between inventors and the relationship between the inventor and the patent. Experimental results show that our method can effectively identify the inventors with high influence in patent data, and that it converges faster than PageRank.展开更多
The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds man...The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds many practical applications in numerous areas such as information dissemination,epidemic immunity,and viral marketing.However,most existing influence maximization algorithms are limited by the“rich-club”phenomenon and are thus unable to avoid the influence overlap of seed spreaders.This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy,named AIGCrank,to identify a set of influential seeds.Specifically,the gravity centrality jointly employs the neighborhood,network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed.We also present a recursive ranking strategy for identifying seed nodes one-byone.Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.展开更多
对学术机构进行客观公正的评价是科研管理过程中不可或缺的工作,基于网络计量学的机构影响力评价成为学术机构评价研究中有别于传统文献计量方法的另外一种有效的评价手段。为了进一步探讨不同的分析策略对网络影响力的评价效果的影响,...对学术机构进行客观公正的评价是科研管理过程中不可或缺的工作,基于网络计量学的机构影响力评价成为学术机构评价研究中有别于传统文献计量方法的另外一种有效的评价手段。为了进一步探讨不同的分析策略对网络影响力的评价效果的影响,文章以我国342所大学作为研究样本,对比分析不同Web数据采集策略下,利用不同评价指标对机构的网络影响力进行评价的可靠性。研究结果表明,机构入链所属的大学域名的数量指标RD_EDU以及学院层面的链接指标Linknet与多个大学排名之间的平均相关系数接近0.8,并且高于以Webometrics Ranking of World Universities和uni Rank为代表的网络影响力排名与ARWU、CARK和CUAA等大学排名之间的相关性强度。展开更多
基金supported by National Basic Research Program of China (2012CB719905)National Natural Science Funds of China (41201404)Fundamental Research Funds for the Central Universities of China (2042018gf0008)
文摘Recently, as location-based social network(LBSN) rapidly grow, point-of-interest(POI) recommendation has become an important way to help people locate interesting places. Nowadays, there have been deep studies conducted on the geographical and social influence in the point-of-interest recommendation model based on the rating prediction. The fact is, however, relying solely on the rating fails to reflect the user's preferences very accurately, because the users are most concerned with the list of ranked point-of-interests(POIs) on the actual output of recommender systems. In this paper, we propose a co-pairwise ranking model called Geo-Social Bayesian Personalized Ranking model(GSBPR), which is based on the pairwise ranking with the exploiting geo-social correlations by incorporating the method of ranking learning into the process of POI recommendation. In this model, we develop a novel BPR pairwise ranking assumption by injecting users' geo-social preference. Based on this assumption, the POI recommendation model is reformulated by a three-level joint pairwise ranking model. And the experimental results based on real datasets show that the proposed method in this paper enjoys better recommendation performance compared to other state-of-the-art POI recommendation models.
基金Project supported by the National Science and Technology Support Plan （No. 2013BAH21B02-01）, Beijing Natural Science Foundation （No. 4153058）, and Shanghai Key Laboratory of Intelligent Information Processing （No. IIPL-2014-004）
文摘Most classic network entity sorting algorithms are implemented in a homogeneous network, and they are not appli- cable to a heterogeneous network. Registered patent history data denotes the innovations and the achievements in different research fields. In this paper, we present an iteration algorithm called inventor-ranking, to sort the influences of patent inventors in heterogeneous networks constructed based on their patent data. This approach is a flexible rule-based method, making full use of the features of network topology. We sort the inventors and patents by a set of rules, and the algorithm iterates continuously until it meets a certain convergence condition. We also give a detailed analysis of influential inventor＇s interesting topics using a latent Dirichlet allocation （LDA） model. Compared with the traditional methods such as PageRank, our approach takes full advantage of the information in the heterogeneous network, including the relationship between inventors and the relationship between the inventor and the patent. Experimental results show that our method can effectively identify the inventors with high influence in patent data, and that it converges faster than PageRank.
基金the National Social Science Foundation of China(Grant Nos.21BGL217 and 18AZD005)the National Natural Science Foundation of China(Grant Nos.71874108 and 11871328)。
文摘The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds many practical applications in numerous areas such as information dissemination,epidemic immunity,and viral marketing.However,most existing influence maximization algorithms are limited by the“rich-club”phenomenon and are thus unable to avoid the influence overlap of seed spreaders.This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy,named AIGCrank,to identify a set of influential seeds.Specifically,the gravity centrality jointly employs the neighborhood,network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed.We also present a recursive ranking strategy for identifying seed nodes one-byone.Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.
文摘对学术机构进行客观公正的评价是科研管理过程中不可或缺的工作,基于网络计量学的机构影响力评价成为学术机构评价研究中有别于传统文献计量方法的另外一种有效的评价手段。为了进一步探讨不同的分析策略对网络影响力的评价效果的影响,文章以我国342所大学作为研究样本,对比分析不同Web数据采集策略下,利用不同评价指标对机构的网络影响力进行评价的可靠性。研究结果表明,机构入链所属的大学域名的数量指标RD_EDU以及学院层面的链接指标Linknet与多个大学排名之间的平均相关系数接近0.8,并且高于以Webometrics Ranking of World Universities和uni Rank为代表的网络影响力排名与ARWU、CARK和CUAA等大学排名之间的相关性强度。
