Mining Social Groups with Weighted Similarity in Campus Wireless Network

With the popularity of wireless networks and the prevalence of personal mobile computing devices, understanding the characteristic of wireless network users is of great significance to the network performance. In this study, system logs from two universities, Dartmouth College and Shanghai Jiao Tong University(SJTU), were mined and analyzed. Every user's log was represented by a user profile. A novel weighted social similarity was proposed to quantify the resemblance of users considering influence of location visits. Based on the similarity, an unsupervised learning method was applied to cluster users. Though environment parameters are different, two universities both form many social groups with Pareto distribution of similarity and exponential distribution of group sizes. These findings are very important to the research of wireless network and social network .

Finding Statistically Significant Communities in Networks with Weighted Label Propagation

Various networks exist in the world today including biological, social, information, and communication networks with the Internet as the largest network of all. One salient structural feature of these networks is the formation of groups or communities of vertices that tend to be more connected to each other within the same group than to those outside. Therefore, the detection of these communities is a topic of great interest and importance in many applications and different algorithms including label propagation have been developed for such purpose. Speaker-listener label propagation algorithm (SLPA) enjoys almost linear time complexity, so desirable in dealing with large networks. As an extension of SLPA, this study presented a novel weighted label propagation algorithm (WLPA), which was tested on four real world social networks with known community structures including the famous Zachary's karate club network. Wilcoxon tests on the communities found in the karate club network by WLPA demonstrated an improved statistical significance over SLPA. Withthehelp of Wilcoxon tests again, we were able to determine the best possible formation of two communities in this network relative to the ground truth partition, which could be used as a new benchmark for assessing community detection algorithms. Finally WLPA predicted better communities than SLPA in two of the three additional real social networks, when compared to the ground truth.

An efficient shortest path approach for social networks based on community structure

Finding the shortest path （SP） in a large-scale network analysis between any two nodes is a tough but very significant task. The SP can help us to analyze the information spreading performance and research the latent relationship in the weighted social network, and so on. As the size of the social network increases, the traditional SP algorithms have poor performance and there is not a suitable algorithm for weighted social network. Some features of the network analysis are beneficial to solve this problem, and community structure ignored by the traditional methods is one of the most important features. In this paper, we propose a shortest path algorithm based on community detection （SPCD） by integrating community detection algorithm with traditional search methods. SPCD constructs a community graph by using community structure to narrow the searching scope. The algorithm presented improves the time efficiency and maintains the accuracy scale of the SR Experimental results on five real-world networks demonstrate the effectiveness of the proposed methods for the SP problem.

Weight Bias: Twitter as a Tool for Opening Dialogue among Broad Audiences

Twitter is a tool for strengthening research knowledge mobilization to the general public. In this article, we highlight how Twitter can be used to open social dialogue about research related topics between users from multiple perspectives, using the topic of weight bias;a cultural issue largely perpetuated by the media. Specifically, Twitter (@UCalgary Body BS) was used by an interdisciplinary research team to under line cases of global news, stories, and policy related to weight bias and/or weight-related issues for a broad audience to consume. We position Twitter as a relevant means for 1) shaping the research lifecycle, 2) increasing community participation and engagement regarding specific research topics, 3) co-creating evolving social dialogues and critique, 4) reaching broader audiences, 5) opening up sites of debate and tension within a topic, and 6) engaging with a topic salient within our society, a topic that saturates the media—weight bias.

Feasibility, effectiveness, and perceptions of an Internet-and incentive-based behavioral weight loss intervention for overweight and obese college freshmen: A mixed methods approach

Challenges inherent with the transition to college are often accompanied by weight gain among college freshmen. Weight gain and duration of obesity increase metabolic syndrome and cardiovascular disease risk in young adulthood, which supports the need for weight loss interventions tailored to college students. The purpose of this investigation was to conduct a mixed methods pilot trial to determine the efficacy and acceptability of a semester long Internet-and incentive-based weight loss intervention for overweight/obese college freshmen. Participants (n = 27, aged >18 yrs, BMI >25) were randomly assigned to a 12-week social cognitive theory (SCT)-based intervention (Fit Freshmen [FF]) or a health information control group. The FF intervention also included modest financial incentives for weight loss. Primary outcomes included body weight/composition, dietary and physical activity (PA) behaviors, and psychosocial measures (i.e. self-efficacy, self-regulation) associated with diet, PA, and weight loss. Students in the FF intervention participated in focus groups to provide qualitative feedback on program structure and design. FF participants demonstrated significant reductions (all group differences

A Heuristic Reputation Based System to Detect Spam Activities in a Social Networking Platform, HRSSSNP

The introduction of the social networking platform has drastically affected the way individuals interact. Even though most of the effects have been positive, there exist some serious threats associated with the interactions on a social networking website. A considerable proportion of the crimes that occur are initiated through a social networking platform [1]. Almost 33% of the crimes on the internet are initiated through a social networking website [1]. Moreover activities like spam messages create unnecessary traffic and might affect the user base of a social networking platform. As a result preventing interactions with malicious intent and spam activities becomes crucial. This work attempts to detect the same in a social networking platform by considering a social network as a weighted graph wherein each node, which represents an individual in the social network, stores activities of other nodes with respect to itself in an optimized format which is referred to as localized data set. The weights associated with the edges in the graph represent the trust relationship between profiles. The weights of the edges along with the localized data set are used to infer whether nodes in the social network are compromised and are performing spam or malicious activities.

数字经济空间关联与区域经济增长——来自中国省域层面的经验证据

文章通过构建有向加权的空间关联网络刻画省域间数字经济空间关联关系,实证检验网络节点特征对区域经济增长的影响。研究发现:2013—2021年,我国数字经济整体发展水平显著提升,但区域间发展差异突出。我国省际数字经济空间关联网络的通达性不强,呈现地理聚集性,具有典型的“核心-边缘”特征,由东部发达地区构成的净受益板块对数字资源的“虹吸效应”明显。此外,网络中节点地位的差异性明显,具有明显的层级分化特征,节点地区有向转变中心性和控制力对区域经济增长有显著的正向影响,说明数字经济产生的区域联动效应和关联溢出效应对提升经济发展水平具有重要的驱动作用。

奶业社会化服务水平评价指标体系构建及其障碍度研究

奶业社会化服务对促进奶业高质量发展具有重要意义,但对其评价指标体系以及发展障碍的研究相对不足,不利于把握其在各地的发展现状与方向,其对奶业高质量发展的促进作用因此受限。文章从饲养、繁育改良、防疫治病等7个方面选取21项指标构建了奶业社会化服务评价指标体系,运用熵权TOPSIS模型、障碍度模型对2011—2020年我国奶制品十大主产区的奶业社会化服务发展水平的时空演变特征及其影响因素进行了分析。结果表明:在奶业社会化服务发展水平层面,山东表现突出,黑龙江、内蒙古等地服务发展水平与奶业产出水平失衡,服务发展水平在地理分布上具有东部地区普遍优于西部地区的特点。在影响因素层面,来自繁育改良服务、供给主体规模等子系统的影响较为显著,尤其是子系统中种畜拥有量、生鲜乳冷藏服务水平、奶牛养殖龙头企业数等指标平均障碍度较高,影响较大。该评价指标体系的建立与障碍度的测算具有重要意义,将有助于早日建立高效且广泛覆盖的奶业社会化服务体系,推进奶业振兴。

高海拔地区铁路施工人员作业疲劳影响因素耦合分析

为了评价多因素耦合作用下高海拔地区铁路工程施工人员作业疲劳状况。首先,建立作业疲劳影响因素指标体系,并根据涌现理论,从工作本身、作业人员、作业环境和组织管理4个维度将耦合作用分为单因素耦合、双因素耦合和多因素耦合;其次,以高海拔地区铁路工程施工项目为例,利用熵权-决策实验室分析(Entropy Weight Method-Decision Making Trial and Evaluation Laboratory, EWM-DEMATEL)法确定指标权重,构建耦合度模型,进而对作业疲劳影响因素间的耦合关系进行定量分析,寻找关键耦合因素;最后,建立施工人员作业疲劳“工-管”耦合系统的系统动力学模型,并动态观测疲劳影响因素对施工人员疲劳水平发展趋势的影响。结果显示,工作本身和组织管理的耦合度最大,且疲劳水平的增长速率在第5周后明显增加。在施工作业过程中,应提前建立有针对性的管理措施来降低疲劳影响因素之间的耦合效应。

野生藏酋猴的体重及其影响因素

体重是体现灵长类动物生活史和生长发育特征的重要指标,可反映其生理状况、行为变化和生态适应等信息。本研究以安徽黄山野生猴谷YA1群的48只藏酋猴为对象,使用地磅秤对群内个体进行连续11个月(2022年7月至2023年5月)的体重监测。研究结果显示,雄性藏酋猴的平均体重为(18.71±2.33)kg,显著高于雌性(11.79±1.97)kg。未成年雄性藏酋猴体重的快速发育期为3~5岁,雌性为2~3岁。成年雌雄体重与个体社会等级间均存在显著的正相关关系(P<0.001)。成年藏酋猴体重存在显著的季节变化(P<0.05),雌雄个体体重均在秋季最高,但是雄性个体在冬季最低,雌性个体春季最低。未怀孕个体体重在不同季节无显著性差异,而怀孕个体的体重从冬季到春季显著降低(P<0.05)。本研究表明,藏酋猴的体重具有显著的雌雄性二型现象,未成年雌雄个体体重的快速发育期存在明显分化,季节和社会等级对藏酋猴体重均具有显著影响。

基于加权概念格与AE的社交知识图谱语义压缩

社交知识图谱具有时效性强、更新频繁快等特点,但也导致内容不断扩充,产生了大量冗余信息。如何删除冗余信息并对其进行语义压缩成为提升社交知识图谱质量的关键。为此,该文提出一种基于加权概念格与自编码器的社交知识图谱语义压缩方法(SCWCL-AE)。首先,将社交知识图谱资源描述框架三元组转换为二进制矩阵,获得同一社交知识图谱不同时间版本的形式背景,并通过信息熵确定实体属性权值,构造社交知识图谱加权概念格;提取满足加权外延社交知识图谱支持度的实体属性分类规则,获得重要且使用频率高的实体摘要。其次,引入无监督自编码器,从无标签的社交知识图谱实体摘要中自动学习抽象特征,以重构损失最小化输出数据,达到压缩知识图谱冗余语义信息的目的。最后,在Freebase、DBpedia、Zhishi.me数据集上进行实验,结果验证了该方法对社交知识图谱语义压缩的有效性。

基于熵权-因子分析对音乐影响的社会网络分析和相似性研究

本文针对音乐影响的社会网络分析和相似性进行研究,首先选取相关数据,综合使用社会网络分析理论、广度优先搜索、因子分析等方法进行建模;然后运用Matlab、Python等软件编程求解,充分利用所获得的音乐数据,从多个方面对音乐的影响力进行了统计描述和量化分析;最后构建了一个合理的、符合实际的衡量音乐影响力的模型,来审视艺术家和流派的进化和发展趋势。研究得出各流派音乐家之间的互相影响会对音乐的变化方向产生关键性影响,同时模型对其他职业的社会影响具有一定的泛化性。

企业交易视角下京津冀城市群网络特征研究

利用企业货物交易数据,通过社会网络及有向加权网络分析等方法解读京津冀城市群网络特征及影响因素。结果表明:(1)企业交易网络多对一的供需结构突出,国有企业带动上下游资源汇聚特征明显。(2)同城货物交易特征突出,城际交易形成以北京为核心,天津、石家庄为次核心,沧州、唐山、保定和廊坊为重要节点,其余为一般节点的等级结构。(3)整体网络以核心辐射为主,核心外围的层级式联系突出,水平多向联系的网络化程度不高,北京—天津、北京—石家庄联系最强;不对称性主要体现为石家庄对省内邻近京津的地区供给,京津与廊坊、保定等有较密切的双向贸易联系。(4)交易联系受城市间地理邻近、技术邻近、产业邻近、经济邻近、制度邻近及人口规模因素的影响。

一般自我效能感在孕妇体重控制中的作用分析

目的探索影响孕妇孕前体重及孕期体重增加(GWG)的相关因素,了解孕妇自身一般自我效能对于其体重控制的直接影响与中介效应。方法于2019年1月至2020年7月对在某社区卫生服务中心建档的900名孕妇进行问卷调查。结果AMOS中介效应检验结果显示,孕妇的孕前个人社会基础、孕前社会心理支持、孕前一般效能感、医生孕期体重管理认可度对孕妇体重控制有直接正向中介效应,而一般自我效能还在社会心理支持水平与GWG和孕前BMI之间起部分中介效应;另外,孕前BMI、孕妇对医生孕期体重管理认可度则在一般自我效能与GWG结果之间起部分中介效应。结论应对低一般自我效能感和低社会心理支持水平孕妇的体重控制情况予以重点关注,制定个性化的干预措施并开展综合干预。

融合社交利益与图注意力网络的同伴互评分数预测

在同伴互评过程中,评估者会因为战略性评估而导致评估分数不准确.本文考虑了评估者之间的社交利益关系,提出了一种融合社交利益与图注意力网络的同伴互评分数预测方法 GAT-SIROAN.该方法由表示评估者与解决方案关系的加权网络SIROAN以及用来预测同伴互评分数的图注意力网络GAT构成.在SIROAN中使用ITSA方法定义了评估者的两个特征:自我评分能力和同伴评分能力,并通过比较这两个特征来获取评估者之间的社交利益因子和关系.在分数预测环节,为了考虑每个节点的重要性,使用自注意力机制来计算节点的注意力系数,以此来提高预测能力.采用最小化其均方根误差来学习网络的参数,从而获取更准确的同伴互评预测分数.GATSIROAN在真实数据集上与平均值、中位数、PeerRank、RankwithTA以及GCN-SOAN这5个基线方法进行了对比实验,结果表明GAT-SIROAN在RMSE指标上均优于基线方法.

民生视角下地铁项目的社会效益评价

为保障地铁项目的可持续发展,从有形和无形效益2方面研究民生视角下地铁项目的社会效益。结合文献分析及专家社会调查,构建地铁项目社会效益评价指标体系;采用熵权-决策试验和评价试验(decision-making trial and evaluation laboratory,DEMATEL)法分别确定各指标的客观权重和主观权重,并将熵权法和DEMATEL法相结合确定组合权重;以青岛地铁13号线为例,采用模糊综合评价法评价其社会效益,验证评价方法的可行性。研究表明:90.43%的受访者对地铁带来的社会效益呈满意态度,地铁开通对节能降碳等有形效益具有显著作用,对宜居、宜业、宜游等无形效益具有贡献作用。

基于二分网络加权投影的急性鼻咽炎中药配伍规律挖掘及组合用药预测

目的从二分网络加权投影角度挖掘中药方剂配伍规律,提出一种新的挖掘中药配伍规律的方法,并预测新型药物组合,为指导临床治疗急性鼻咽炎用药提供依据。方法以中医药综合数据库(TCMID)中的急性鼻咽炎方剂数据为数据源,通过提取方剂和药物构建二分网络,继而使用加权投影得到药物网络投影图,结合二分网络加权投影进行社会网络分析,基于皮尔逊相关性进行系统聚类的研究方法挖掘中药“君臣佐使”的配伍规律,并使用链路预测进行核心药物预测。结果二分网络加权投影与皮尔逊相关性进行系统聚类分析相结合的方式在中药方剂的配伍规律研究中作用显著。在链路预测中,选用11个链路预测指标,区分加权与无权算法后最终计算出的加权指标曲线下面积(AUC)大于无权指标,且在加权指标中,AUC最大指标为网络资源分配指标,预测出7组药物组合,包括白头翁与毛诃子、安息香与石椒草、白花茶与附子等。结论二分网络加权投影方法在揭示中药配伍规律与药物组合预测方面具有一定实用性和有效性。

湖北省十堰市恒牙龋病患者伤残权重现状研究

目的了解湖北省十堰市恒牙龋病患者伤残权重(disability weight,DW)现状。方法对湖北医药学院附属东风口腔医院及其两家分门诊的恒牙龋病患者进行问卷调查,并选择恒牙龋痛作为后遗症进行研究,通过患者EQ-5D VAS自评及调查者赋值、专家赋值三种方式获取DW。采用Mann-Whitney U检验及Kruskal-Wallis H检验进行单因素分析、多元线性回归进行多因素分析。结果共220名患者参与调查,年龄多为21~35岁,女性和学生群体居多。患者EQ-5D VAS自评DW[0.300(0.200,0.500)]、调查者赋值DW[0.220(0.096,0.400)]、专家赋值DW[0.220(0.096,0.400)]。单因素分析显示,不同年龄、月收入、学历的患者其自评DW、调查者赋值DW、专家赋值DW差异均具有统计学意义。多因素分析显示年龄越大,DW越大,患者失能状况越严重;学历越高,月收入越高,DW越小,患者失能状况越轻(P<0.05)。结论湖北省十堰市不同赋值方法测算的恒牙龋病患者DW存在差异,年龄大、学历低、月收入低是影响恒牙龋病患者DW的危险因素,当地公共卫生政策制定时应关注该类人群。

基于因子权重法的地方标准社会效益评估方法研究

标准实施效果评估是标准化工作的重要组成部分。文中以福建省地方标准《锅炉用固体废弃物燃料性能评价规则》为研究对象,从评估准备、评估实施、数据资料收集、评估结果测算及应用等方面,采用分级评价法与因子权重法相结合,对其社会效益评估方法开展研究,为地方标准社会效益评估提供方法和过程参考。

融合用户传播风险和节点影响力分析的虚假信息传播控制方法

在线社交网络中虚假信息传播蔓延成为当前网络空间安全治理面临的重要挑战。提出一种融合用户传播风险和节点影响力分析的虚假信息传播控制方法DDC-UPRNI(disinformation diffusion control method integrating user propagation risk and node influence analysis)。综合考虑虚假信息传播特征空间的多样性和复杂性,通过自注意力机制实现用户传播虚假信息行为维度、时间维度和内容维度特征的嵌入表示,运用改进的无监督聚类K-means++算法实现不同用户传播风险等级的自动划分;设计一种自适应加权策略实现对离散粒子群优化算法的改进,进而提出一种基于离散粒子群优化的虚假信息传播关键节点选取方法,用于从具有特定传播风险等级的用户节点集合中选取若干个具有影响力的控制驱动节点,从而实现精准、高效的虚假信息传播控制;基于现实在线社交网络平台上开展试验,结果表明,所提出的DDC-UPRNI方法与现有算法相比,在控制效果和时间复杂度等重要指标上具有明显优势。该方法为社会网络空间中的虚假信息管控治理提供重要参考。