Unpacking (un)conscious social class bias in faculty hiring processes in Chile: PhD prestige granting university and network

Social class of origin is apparently an imperceptible attribute among doctorate holders seeking academic jobs. Yet, recent studies in different countries reveal that social class of origin may still be influencing the chances of PhD holders from low social class being hired at prestigious universities. Drawing from the theory of social and cultural reproduction, normative ‘fair’ academic hirings frameworks, and qualitative evidence collected in Chile, this research identifies the mechanisms that trigger (un)conscious social class bias in the stages of recruitment and selection of candidates in seven academic departments in economics and industrial engineering (46 interviews). Findings did not prove explicit classism manifestations, but practices of inclusion/exclusion of candidates based on the prestige of PhD-granting universities, and networks. These reproduce the relationship between social class and unequal chances of being connected to prestigious universities. Recommendations to address (un)conscious social class bias in academic hiring are discussed.     

What Tahrir Square Has Done for Social Media: A 2012 Snapshot in the Struggle for Political Power in Egypt

While the debate rages on the role of social media technologies in the initial days of the Egyptian revolution of 2011, a more relevant research question today is the role of social media within an increasingly contested and turbulent political sphere. This article identifies three key modes by which social media is being exploited to impact political power, and uncovers the salience of each of these through 2 years of multisited ethnographies and interviews. First, I argue that political actors across the political spectrum, from liberals to Muslim Brotherhood (Ikhwan), are using technologies to build wider ranging, heterogeneous, and diverse networks of supporters, expanding their base from a more historically homogeneous core support group. Second, I argue that actors are working to build bridges between older and newer media platforms, recognizing that each platform is increasingly being shaped by the other. Finally, I describe some of the ways that technology is being used by activists to subvert their competition, promoting campaigns of misinformation and hacking at the expense of others.     

Creating value in ecosystems: Crossing the chasm between knowledge and business ecosystems

Policy makers take initiatives to stimulate knowledge ecosystems in technology hotspots. It is implicitly assumed that these ecosystems will lead to value networks through which the participating companies can realize a competitive advantage. Value networks refer to business ecosystems where the value proposition is offered by a group of companies which are mutually complementary. The strategy literature suggests that business ecosystems lead to competitive advantages for each of the partners in the ecosystem. Based on a unique hand-collected database of 138 innovative start-ups in the region of Flanders, we analyze the knowledge and business ecosystem and the financial support network. We find that the knowledge ecosystem is well structured and concentrated around a number of central actors while the business ecosystem is almost non-existent at the local level. Further, we find that the financial support network is almost 100% publicly backed and fails to bridge the knowledge and business ecosystem. The implications for policy makers who tend to focus on the development of local ecosystems are discussed.     

相遇信息估算概率的机会网络路由协议

在分析现有机会网络转发策略的基础上,根据Prophet和Spray and wait协议的特点提出一种相遇信息估算转发概率的机会网络路由协议(BPAS),BPAS以节点相遇的频率、相遇的连通时长和断连时长作为参量引入转发概率的计算,消息转发给转发概率大的节点,使得消息朝更靠近目的节点方向发送。仿真结果表明,BPAS协议与Epidemic协议、Prophet协议和Spray and wait协议相比,能获得更高的消息交付率和更低的通信开销,消息总转发次数更少。实验证明BPAS协议能适应于节点密度较大的机会网络环境。     

基于Noisy-or gate 和贝叶斯网络的研发项目风险评估方法

在没有充足样本数据时,为有效地管理各种风险,本文提出了一个全新的研发项目风险评估模型,这个模型主要是基于Noisy-or gate 和贝叶斯网络进行评估。该模型在贝叶斯网络节点满足构成Noisy-or gate模型的前提下,利用历史数据或专家判断得到有效的网络参数,进而推断出每一项风险因素发生的概率,结合风险影响权重得到综合风险影响值,并对比得到高风险因素,为风险管理提供依据。通过与AHP方法评估结果对比表明,该模型可以准确地评估研发项目的风险,从而提高风险管理的效率。     

重构强弱关系：在线社交网络中的三维关系模型及应用

关系的概念是社会网络分析的核心,关系强度的测量建立在强弱关系理论基础之上,随着在线社交网络的发展,关系的细分更加必要,三维关系模型旨在改进静态、二元的强弱关系模式,从三个维度对关系强度进行重新构造,即心理维度、结构维度及工具维度。应用文献分析和实证分析的方法,建立了在线网络中的关系测量模型,结果表明,通过量化在线网络中用户的互动行为,可以将每个维度的关系强度归一化为 [0,1] 的连续变量,同现有方法相比,三维关系可以细分关系图谱,在设计SNS功能、隐私控制、朋友推荐及信息扩散等方面,三维关系模型无疑具有重要的理论指导作用。     

基于专利合作网络的北京国际科技合作分析

以美国专利商标局申请库收录的2001-2013年北京国际科技合作专利为数据源,运用文献计量和社会网络的方法对北京国际科技合作专利的逐年发展情况,主要合作国家、重点科研机构、活跃人才以及热点领域的合作情况作出全面的分析和系统的解读,揭示了北京国际科技合作专利快速增长等现状特征,探讨了当前北京国际科技合作在知识产权共享、企业创新能力等方面的问题,从政府的支持力度和要素流动等角度提出了建议对策。     

基于均衡理论的服务覆盖网络路由算法研究

基于Wardrop均衡理论提出了一种跨域的自适应的多下一跳路由算法,仿真实验表明该算法在满足用户QoS需求的同时,能在SON经营者和ISP提供商双方利益博弈的过程中找到均衡点,既均衡网络负载提高网络利用率,又使SON经营者的收益最大化.     

No inventor is an island: Social connectedness and the geography of knowledge flows in the US

Do informal social ties connecting inventors across distant places promote knowledge flows between them? To measure informal ties, we use a new and direct index of social connectedness of regions based on aggregate Facebook friendships. We use a well-established identification strategy that relies on matching inventor citations with citations from examiners. Moreover, we isolate the specific effect of informal connections, above and beyond formal professional ties (co-inventor networks) and geographic proximity. We identify a significant and robust effect of informal ties on patent citations. Further, we find that the effect of geographic proximity on knowledge flows is entirely explained by informal social ties and professional networks. We also show that the effect of informal social ties on knowledge flows is greater for new entrepreneurs or ‘garage inventors’, for older or ‘forgotten’ patents, and for flows across distant technology fields. It has also become increasingly important over the last two decades.     

Contrastive and attentive graph learning for multi-view clustering

Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consistency can achieve fine-modeling of consensus information in multiple views. Towards this end, we propose a novel Contrastive and Attentive Graph Learning framework for multi-view clustering (CAGL). Specifically, we design a contrastive fine-modeling in multi-view graph learning using maximizing the similarity of pair-view to guarantee the consistency of multiple views. Meanwhile, an Att-weighted refined fusion graph module based on attention networks to capture the capacity difference of different views dynamically and further facilitate the mutual reinforcement of single view and fusion view. Besides, our CAGL can learn a specialized representation for clustering via a self-training clustering module. Finally, we develop a joint optimization objective to balance every module and iteratively optimize the proposed CAGL in the framework of graph encoder–decoder. Experimental results on six benchmarks across different modalities and sizes demonstrate that our CAGL outperforms state-of-the-art baselines.