Kinetic Modeling of an Opinion Model on Social Networks

It is commonly accepted that, on social networks, the opinion of the agents with a higher connectivity, i.e., a larger number of followers, results in more convincing than that of the agents with a lower number of followers. By kinetic modeling approach, a kinetic model of opinion formation on social networks is derived, in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion exchange process is governed by a Sznajd type model with three opinions, ±1, 0, and the social network is represented statistically with connectivity denoting the number of contacts of a given individual. The asymptotic mean opinion of a social network is determined in terms of the initial opinion and the connectivity of the agents.

A Personalized Search Model Using Online Social Network Data Based on a Holonic Multiagent System

Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.

Evaluation and optimization analysis of high-speed rail network structure in Northeast China under the background of northeast revitalization

The construction of high-speed rail(HSR)network has promoted the social-economic ties of cities,accelerated the compression of time and space,and changed the pattern of regional development.In this paper,with the adoption of the operation frequency data of HSR from 12306 website,and based on the HSR connection strength model and social network analysis model,as well as according to the HSR connection strength,HSR network density,centrality,agglomeration subgroup,and other indicators,we analyzed the characteristics of HSR network structure in Northeast China.Results show that the number of HSR cities in Northeast China is small,cities in HSR network generally exhibit weak connectivity,and the existence of HSR network marginalizes cities such as Ulanhot,Baicheng,and Songyuan,which significantly reduce the overall network connectivity of Northeast China.The overall centrality of HSR network in Northeast China is characterized by“one axis,four edges”;specifically,the one axis is located in Harbin-Dalian transportation line and the four edges are located on both sides of the main axis of Harbin-Dalian transportation line.Eight agglomeration subgroups(four double city subgroups and four multi city subgroups)have formed in Northeast China.The core status of Shenyang in HSR network is improved significantly,and“one axis and two wings”HSR network in Liaoning Province is improved significantly.With the gradual expansion of Chaoyang-Fuxin,Dandong-Benxi,and Jilin-Yanji branch networks,the“point axis”HSR network mode in Northeast China has gradually developed and matured.In the future,it is recommended to rely on eight agglomerating subgroups to encrypt HSR network structure,create secondary node central cities,and gradually build a new pattern of opening up in Northeast China.

Personalized Privacy Protecting Model in Mobile Social Network

With the rapid development of the new generation of information technology,the analysis of mobile social network big data is getting deeper and deeper.At the same time,the risk of privacy disclosure in social network is also very obvious.In this paper,we summarize the main access control model in mobile social network,analyze their contribution and point out their disadvantages.On this basis,a practical privacy policy is defined through authorization model supporting personalized privacy preferences.Experiments have been conducted on synthetic data sets.The result shows that the proposed privacy protecting model could improve the security of the mobile social network while keeping high execution efficiency.

Recommending Personalized POIs from Location Based Social Network

Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.

Perspectives for Sharing Personal Information on Online Social Networks

The goal of this research was to study how people feel about sharing personal information on social networks. The research was done by interviews;50 people were interviewed, mostly from China's Mainland, Hong Kong, and Finland. This paper presents the included 12 questions and discusses the collected answers. It was discovered, e.g., that 38 out of the 50 answerers use social media every day and share versatile personal information on the Internet. Half of the answerers also share information about other people on the Internet. It was also discovered that compared to male answerers, the female answerers were more active in sharing information about other people. There was a significant variety in opinions: what should be the age limit for sharing personal information online, while 22 out of the 50 answerers felt that there is no need for an age limit at all. According to the answers, only a few people use social media for making new friends. Instead, an important reason for using social media is that their existing friends are using. An interesting finding was that the answerers see the Internet as a part of the real world;the privacy that you have on the Internet is the privacy that you have in the real world.

Privacy disclosure in Chinese social networking sites:An investigation of Renren users

Purpose:This study was conducted to investigate the current situation of privacy disclosure(in the Chinese social networking sites.Design/methodology/approach:Data analysis was based on profiles of 240 college students on Renren.com,a popular college-oriented social networking site in China.Users’ privacy disclosure behaviors were studied and gender difference was analyzed particularly.Correlation analysis was conducted to examine the relationships among evaluation indicators involving user name,image,page visibility,message board visibility,completeness of education information and provision of personal information.Findings:A large amount of personal information was disclosed via social networking sites in China.Greater percentage of male users than female users disclosed their personal information.Furthermore,significantly positive relationships were found among page visibility,message board visibility,completeness of education information and provision of personal information.Research limitations:Subjects were collected from only one social networking website.Meanwhile,our survey involves subjective judgments of user name reliability,category of profile images and completeness of information.Practical implications:This study will be of benefit for college administrators,teachers and librarians to design courses for college students on how to use social networking sites safely.Originality /value:This empirical study is one of the first studies to reveal the current situation of privacy disclosure in the Chinese social networking sites and will help the research community gain a deeper understanding of privacy disclosure in the Chinese social networking sites.

Exploiting Geo-Social Correlations to Improve Pairwise Ranking for Point-of-Interest Recommendation

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.

How much do social connections matter in fundraising outcomes?

This study examines the role of social connections and network centrality in attracting funders to crowdfunding campaigns.We classify social connections as either external(e.g.,Facebook)or internal(e.g.,investing in online platforms through resource exchange).Drawing from the 108,463 crowdfunding campaigns on the online platform Kickstarter from April 21,2009,to July 24,2019,we apply external linkages and online followers to estimate the effect of external social connections.We construct a digraph network for the internal social connections and use PageRank,HITS,and centrality to obtain the weights of the nodes.Next,we compare the performance change of several prediction algorithms by feeding social connection-related variables.This study has several findings.First,for external social connections,having more online followers improves the funding success rate of a campaign.Second,for internal social connections,only authority and degree in centrality positively affect the number of funders and the campaign’s financing progress among the weights of the nodes.Third,using social connection variables improves the prediction algorithms for funding outcomes.Fourth,external social connections exert greater funding outcomes than internal social connections.Fourth,entrepreneurs should extend their external social connections to their internal social connections,and network centrality expedites project financing.Fifth,the effect of social connections on fundraising outcomes varies among the campaign categories.Fundraisers who are online influencers should leverage their online social connections,notably for the project categories that matter.

Recommending Who to Follow on Twitter Based on Tweet Contents and Social Connections

In this paper, we examine methods that can provide accurate results in a form of a recommender system within a social networking framework. The social networking site of choice is Twitter, due to its interesting social graph connections and content characteristics. We built a recommender system which recommends potential users to follow by analyzing their tweets using the CRM114 regex engine as a basis for content classification. The evaluation of the recommender system was based on a dataset generated from real Twitter users created in late 2009.

Turning into a “Godparent”: How adult volunteers negotiate their personal life to become a mentor for “Unaccompanied Refugee Minors”

This article looks into how volunteers deal with their biographies and social embeddedness to make sense of their engagement in mentoring before they are matched. It draws on a qualitative investigation on a community-based pilot youth mentoring program for “unaccompanied refugee minors” in Austria. This article reveals how already trained, local adults actively relate to “family,”“migration” and “previous activities” in their meaning-making. It shows how they negotiate their personal life and existing relationships in the process of turning into a future “godparent.” The discussion of findings against the state of the art leads the way to two heuristic claims: firstly, the study provides grounded arguments for an extension of the conventional mentoring concept on the side of the mentor. Secondly, for a more relational and processual approach towards the mentors’ side, both biographical and social network dimensions need to be integrated in methods and designs of youth mentoring research.

商会关系、声誉机制与代理成本

以中国证券市场2004—2021年A股上市公司为研究对象,研究企业家(董事长或总经理)商会关系资本与企业代理成本之间的关系,并进一步考察企业家个人声誉机制对上述关系的潜在影响。研究结果表明:企业家商会关系能够降低企业代理成本,且企业家商会任职层级越高、任职种类越多,抑制作用越显著;当企业家拥有人大代表或政协委员的政治身份以及兼任其他上市公司董事的社会身份等个人声誉时,商会关系对企业代理成本的抑制作用较弱;渠道检验发现,激励机制和监督机制是企业家商会关系作用于企业代理成本的重要渠道;进一步分析发现,企业家行业协会关系、国家及省级商会关系对代理成本的抑制作用更加明显,在非国有企业中企业家商会关系对代理成本的抑制作用更加明显。研究结论丰富和拓展了关系社会资本的微观经济后果及作用机制,为深入理解商会关系文化对微观企业决策行为的作用机制提供了初步的理论分析框架和经验证据。

铁路网络联系对城市土地绿色利用效率的影响研究——以长三角地区为例

研究目的:探究铁路网络联系对城市土地绿色利用效率的影响及空间溢出效应,为依托铁路交通发展促进土地绿色利用效率提升提供科学依据。研究方法:超效率SBM模型,社会网络分析方法,空间面板模型。研究结果:(1)长三角城市铁路联系强度和土地绿色利用效率均呈现显著的增长趋势,土地绿色利用效率存在着较为明显的区域差异。(2)铁路联系强度的提高促进了本城市土地绿色利用效率的提升,与综合铁路联系相比,高铁联系对本城市土地绿色利用效率的提升作用更加明显。(3)铁路联系的加强促进了本城市产业结构合理化水平的提升和创新产出的增长,进而对土地绿色利用效率产生影响。高铁联系在促进本城市产业结构合理化水平提升和创新产出增长方面的作用更加明显。(4)城市对外铁路联系强度的提高产生了负向的空间溢出效应,抑制了邻近城市土地绿色利用效率的提升。研究结论:应充分发挥铁路建设在优化产业结构、促进创新方面的作用,依托铁路网络加强区域内经济技术合作、发挥各城市比较优势,以推动区域土地绿色利用效率的整体性提升。

科技创新新型举国体制下多元投资主体网络关联模式

科技创新新型举国体制是应对错综复杂国际环境和全面建成社会主义现代化强国使命任务的重大制度安排,厘清科技创新新型举国体制的多元投资主体关系对于推动有为政府与有效市场相结合具有重要意义。本文在分析科技创新新型举国体制的制度创新基础上,归纳不同实践场景下多元投资主体的基本格局,利用社会网络分析的网络拓扑结构、中心性、核心边缘分析和密度分析手段,对多元投资主体的网络结构、网络中心度以及网络的整体特征进行研究。结果表明,科技创新新型举国体制能充分调动市场与社会力量,各投资主体之间的信息、资源等流动加快,各投资主体间的联系更加紧密,企业在科技创新新型举国体制中将发挥更重要的作用。

满足个性化差分隐私的社交网络图生成方法

【目的】针对现有本地化差分隐私社交网络图生成算法中直接扰动邻居列表的方法会导致引入过多噪声且隐私保护程度不均衡的问题,提出了一种满足个性化的本地差分隐私社交网络图生成方法(GPDP)。【方法】首先,使用传统的社区发现算法Louvain对原始社交网络图进行划分,保留社区信息;其次,对于划分后的社区根据其社区内部平均权重度比值作为新的隐私预算参数分配给每个节点;然后,每个节点根据新的隐私预算各自扰动其邻居列表,同时利用随机邻接位向量(RABV)方法降低通讯成本;最后合并邻居列表形成生成图。【结果】通过在真实数据集上的实验结果表明,该算法在发布合成图数据时保证了数据隐私性和可用性的均衡,同时保留了更多的社区结构信息。

基于社会-生态功能复合与SPCA的城市绿地生态网络重要源地识别

构建基于社会-生态功能复合的城市生态源地综合评价指标体系,以上海市闵行区城市绿地为对象,运用GIS、RS、Invest及景观连接度模型等方法计算指标,采用SPCA确定指标权重,通过空间加权计算和重分类识别重要源地。结果表明:(1)在424个备选源地中,极重要、重要、一般、不重要和极不重要等级面积占比分别为16.2%、16.1%、25.3%、23.0%和19.4%;(2)共识别极重要、重要源地139个,总面积18.61km^(2),平均面积0.134km^(2),占备选源地总面积的35.6%,占全区总面积的5.0%;(3)区域绿地是重要源地的主体,其次是公园绿地和附属绿地;(4)在规划实践中,可依托区域绿地开展绿林农湿多要素复合,提升源地总体规模和服务功能。研究成果可为上海及长三角其他城市精准识别城市绿地生态网络重要源地提供借鉴。

成渝地区双城经济圈物流空间联系及其网络结构研究

区域物流对经济发展至关重要。为揭示当前成渝地区双城经济圈(以下称成渝地区)物流联系强度及其网络空间结构,本文基于地理空间联系视角,在2009—2019年研究期间以成渝地区16个地级及以上城市为研究对象,采用引力模型构建成渝地区物流网络,通过社会网络分析法测算网络密度、网络中心度以及凝聚子群,进而对其联系强度、空间布局等进行深入分析。结果表明:在本研究时间范围内,成渝地区城市物流质量水平空间分异显著,物流联系强度差异大;网络密度从0.9023升至0.9042,处于较高密度水平,波动幅度小,聚合度强,物流联系密切,但成渝地区内部网络密度失衡,中部密度最高,北部密度较中部稍低,西部密度最低;整体上物流网络呈现以成都、重庆为核心相向发展的放射状网络结构。

CSCL环境下促进协作的小组角色支架设计与实践

促进学习者协作是CSCL研究的核心问题之一。已有研究从不同角度提出策略来促进小组的协作效果,本研究在构建多维学习者特征模型的基础上,依据“组间同质、组内异质”原则提出CSCL个性化分组策略,利用SNA构建小组角色支架,将个性化分组策略和角色支架应用于CSCL环境下的小组交互活动中。为探究该支架对协作小组的教学效果,本研究以某高校《计算机导论》课程为例,开展实践教学,从在线讨论参与度和交互质量两方面验证其在小组活动实施中的有效性。结果表明,协作式学习可通过制定合理的个性化教学分组模型来营造融洽的学习氛围;采用角色支架干预的讨论形式能够提高学习者参与小组学习的积极性,提升协作学习质量。该策略为促进CSCL环境下的小组协作学习提供一定的借鉴和参考。

一种基于图神经网络的社会化推荐算法

现有的社会化推荐算法大多着眼于用户购买或点击等单一的交互行为,并未同时考虑收藏、浏览等多种不同的交互行为.而且当前的社会化推荐算法重点只关注推荐的准确性,忽略了推荐结果的可解释性.针对以上问题,提出了一种基于图神经网络的社会化推荐算法SRGN,将用户的社交关系和物品间客观存在的语义联系以特定的方式注入到算法架构中,并且利用消息传递的方式实现交互的多行为联合编码,从而提升推荐的准确性.此外,设计了可解释模块为推荐结果提供推荐的理由.在两个真实数据集上与其他8种算法进行对比实验,结果表明提出的算法在推荐性能和用户友好性上具有明显的优势.

国家物流枢纽城市物流空间联系及其网络特征分析

为深入研究国家物流枢纽城市物流空间联系网络的结构特征,基于110个国家物流枢纽城市数据,采用修正引力模型、社会网络分析法对枢纽城市间的物流空间联系及其网络特征进行分析。结果发现,全国、各区域的枢纽城市物流空间联系网络密度普遍较低,说明枢纽城市间的物流联系尚不够紧密;度中心度较高的枢纽城市也是中介中心度排名靠前的城市,这些城市成为全国或区域内物流资源的辐射极,带动其他枢纽城市联动发展;网络在空间上呈现“核心-半核心-边缘”结构,“轴辐式”层级结构明显;枢纽城市群集特征明显,共分为8大子群,但子群间的互动不明显;上海、北京、深圳、广州等物流城市具有网络结构洞优势,秦皇岛、南通、嘉兴等枢纽城市则相反。