Phishing scams detection via temporal graph attention network in Ethereum

Recently, phishing scams have become one of the most serious types of crime involved in Ethereum, the second-largest blockchain-based cryptocurrency platform. The existing phishing scams detection techniques for Ethereum mostly use traditional machine learning or network representation learning to mine the key information from the transaction network and identify phishing addresses. However, these methods typically crop the temporal transaction graph into snapshot sequences or construct temporal random wanderings to model the dynamic evolution of the topology of the transaction graph. In this paper, we propose PDTGA, a method that applies graph representation learning based on temporal graphs attention to improve the effectiveness of phishing scams detection in Ethereum. Specifically, we learn the functional representation of time directly and model the time signal through the interactions between the time encoding function and node features, edge features, and the topology of the graph. We collected a real-world Ethereum phishing scam dataset, containing over 250,000 transaction records between more than 100,000 account addresses, and divided them into three datasets of different sizes. Through data analysis, we first summarized the periodic pattern of Ethereum phishing scam activities. Then we constructed 14 kinds of account node features and 3 kinds of transaction edge features. Experimental evaluations based on the above three datasets demonstrate that PDTGA with 94.78% AUC score and 88.76% recall score outperforms the state-of-the-art methods.     

Harvesting random embedding for high-frequency change-point detection in temporal complex systems

Recent investigations have revealed that dynamics of complex networks and systems are crucially dependent on the temporal structures. Accurate detection of the time instant at which a system changes its internal structures has become a tremendously significant mission, beneficial to fully understanding the underlying mechanisms of evolving systems, and adequately modeling and predicting the dynamics of the systems as well. In real-world applications, due to a lack of prior knowledge on the explicit equations of evolving systems, an open challenge is how to develop a practical and model-free method to achieve the mission based merely on the time-series data recorded from real-world systems. Here, we develop such a model-free approach, named temporal change-point detection (TCD), and integrate both dynamical and statistical methods to address this important challenge in a novel way. The proposed TCD approach, basing on exploitation of spatial information of the observed time series of high dimensions, is able not only to detect the separate change points of the concerned systems without knowing, a priori, any information of the equations of the systems, but also to harvest all the change points emergent in a relatively high-frequency manner, which cannot be directly achieved by using the existing methods and techniques. Practical effectiveness is comprehensively demonstrated using the data from the representative complex dynamics and real-world systems from biology to geology and even to social science.     

User-aware multilingual abusive content detection in social media

Despite growing efforts to halt distasteful content on social media, multilingualism has added a new dimension to this problem. The scarcity of resources makes the challenge even greater when it comes to low-resource languages. This work focuses on providing a novel method for abusive content detection in multiple low-resource Indic languages. Our observation indicates that a post’s tendency to attract abusive comments, as well as features such as user history and social context, significantly aid in the detection of abusive content. The proposed method first learns social and text context features in two separate modules. The integrated representation from these modules is learned and used for the final prediction. To evaluate the performance of our method against different classical and state-of-the-art methods, we have performed extensive experiments on SCIDN and MACI datasets consisting of 1.5M and 665K multilingual comments, respectively. Our proposed method outperforms state-of-the-art baseline methods with an average increase of 4.08% and 9.52% in the F1-score on SCIDN and MACI datasets, respectively.     

社会网络嵌入、双元均衡创新与国际创业研究

双元均衡创新对于国际创业具有重要作用,且二者之间的关系受到社会网络的影响。本文基于组织双元、社会网络等理论,选取中国上市公司中的天生国际化企业2011-2020年的面板数据,探究双元均衡创新的平衡维度、互补维度对天生国际化企业国际创业绩效的影响,以及社会网络中心度与结构洞的调节效应。研究发现：双元均衡创新的平衡维度和互补维度均对天生国际化企业国际创业绩效具有正向影响,社会网络中心度和结构洞均负向调节互补维度与国际创业绩效之间的关系,而对平衡维度与国际创业绩效之间的关系的无调节作用。研究结论在一定程度上拓展了双元均衡创新的研究视角,并完善了社会网络、双元均衡创新以及国际创业之间的内在逻辑。     

Social influence based community detection in event-based social networks

In this paper, we focus on the problem of discovering internally connected communities in event-based social networks (EBSNs) and propose a community detection method by utilizing social influences between users. Different from traditional social network, EBSNs contain different types of entities and links, and users in EBSNs have more complex behaviours. This leads to poor performance of the traditional social influence computation method in EBSNs. Therefore, to quantify the pairwise social influence accurately in EBSNs, we first propose to compute two types of social influences, i.e., structure-based social influence and behaviour-based social influence, by utilizing the online social network structure and offline social behaviours of users. In particular, based on the specific features of EBSNs, the similarities of user preference on three aspects (i.e., topics, regions and organizers) are utilized to measure the behaviour-based social influence. Then, we obtain the unified pairwise social influence by combining these two types of social influences through a weight function. Next, we present a social influence based community detection algorithm which is referred to as SICD. In SICD, inspired by the nonlinear feature learning ability of the autoencoder, we first devise a neighborhood based deep autoencoder algorithm to obtain nonlinear community-oriented latent representations of users, and then utilize the k-means algorithm for community detection. Experimental results conducted on real-world dataset show the effectiveness of our proposed algorithm.     

Exploring connections to unrelated social clusters in a socio temporal communication network

In this study, we first explore whether individuals with the greatest number of weak ties to others will have more connections to a greater number of unrelated social clusters. Secondly, we explore whether individuals with the greatest number of weak ties to others will serve as bridges between isolated social clusters. Thirdly, we analyse whether the level of call activity is dependant on different types of social network structures (i.e. strong ties and weak ties). Here, we investigate the effects of social ties on mobile phone usage behaviour. The research conceptual model represents the relationship between three independent variables and one dependant variable. The three independent variables — (i) call activity; (ii) connection to unrelated social clusters; and (iii) social bridges between unrelated social clusters. We suggest that each of the three independent variables has an impact on the way individuals use mobile phone devices. By exploring the MIT Reality Mining Data, we first found a trend where the individuals who have the greatest number of strong social ties to others display the highest levels of call activity. On the contrary, individuals who have a modest number of strong ties, but have a high number of acquaintances show lower levels of call activity purely because the weak tie relationships do not require as much maintenance as the strong ties. Secondly, we visualise where unrelated social clusters within a social network displayed some connections to one another. We propose that the majority of these connections interlinking such unrelated social clusters would be weak ties. Thirdly, we discover that the individuals who display the greatest number of weak tie relationships are linked to the individuals in various social clusters.     

Vulnerable community identification using hate speech detection on social media

With the rapid development in mobile computing and Web technologies, online hate speech has been increasingly spread in social network platforms since it's easy to post any opinions. Previous studies confirm that exposure to online hate speech has serious offline consequences to historically deprived communities. Thus, research on automated hate speech detection has attracted much attention. However, the role of social networks in identifying hate-related vulnerable community is not well investigated. Hate speech can affect all population groups, but some are more vulnerable to its impact than others. For example, for ethnic groups whose languages have few computational resources, it is a challenge to automatically collect and process online texts, not to mention automatic hate speech detection on social media. In this paper, we propose a hate speech detection approach to identify hatred against vulnerable minority groups on social media. Firstly, in Spark distributed processing framework, posts are automatically collected and pre-processed, and features are extracted using word n-grams and word embedding techniques such as Word2Vec. Secondly, deep learning algorithms for classification such as Gated Recurrent Unit (GRU), a variety of Recurrent Neural Networks (RNNs), are used for hate speech detection. Finally, hate words are clustered with methods such as Word2Vec to predict the potential target ethnic group for hatred. In our experiments, we use Amharic language in Ethiopia as an example. Since there was no publicly available dataset for Amharic texts, we crawled Facebook pages to prepare the corpus. Since data annotation could be biased by culture, we recruit annotators from different cultural backgrounds and achieved better inter-annotator agreement. In our experimental results, feature extraction using word embedding techniques such as Word2Vec performs better in both classical and deep learning-based classification algorithms for hate speech detection, among which GRU achieves the best result. Our proposed approach can successfully identify the Tigre ethnic group as the highly vulnerable community in terms of hatred compared with Amhara and Oromo. As a result, hatred vulnerable group identification is vital to protect them by applying automatic hate speech detection model to remove contents that aggravate psychological harm and physical conflicts. This can also encourage the way towards the development of policies, strategies, and tools to empower and protect vulnerable communities.     

基于社会网络分析的校企专利合作网络结构研究——长三角城市群高校的省际比较

基于国家知识产权局公开的2012—2016年长三角地带的上海市、江苏省、浙江省、安徽省共71所高校与企业合作申请的发明专利数据,运用社会网络分析方法,分析专利合作网络空间分布特点与网络结构特征。研究发现,四省之间专利合作关系的合作程度存在着较大差异,表现出空间分布不均衡的特点:江苏省在校企专利合作总数、进入社会网络的高校数量以及高校与企业的合作频次方面都居于首位,上海市次之,浙江省位居第三,安徽省最低。并且"985/211"类型的高校比普通高校的校企合作数量多,理工科/综合类高校比师范院校、财经院校的校企合作数量多。     

Social influence on endorsement in social Q&A community: Moderating effects of temporal and spatial factors

This study examines the impacts of social influence, which are manifested by popularity and source credibility, on social endorsement in social Q&A community; and how the relationship is impacted by temporal-spatial factors. By collecting panel data from a large platform, the results of an econometric model demonstrate that popularity and source credibility are positively associated with social endorsement. With respect to the moderation effects, the results further show that time distance strengthens the effect of popularity on the social endorsement, but undermines the effect of source credibility; while crowdedness plays the role that strengthens the impact of popularity on the social endorsement, it has no significant moderating effect on the relationship between source credibility and social endorsement. Both theoretical and practical implications are discussed.     

风险投资网络社群结构、信息传播与认知临近性

   本文主要研究中国风险投资网络社群结构影响信息传播。遵循社群结构在认知临近性的影响下改变信息传播的逻辑思路,提出了风险投资网络社群结构的三个维度和信息传播的两种类型,采用实证分析法分别检验。研究发现：社群凝聚性对两种信息传播都是负向影响,地位差异和协调性对两种信息传播都是正向影响。凝聚性比地位差异影响项目信息传播更显著,地位差异比协调性影响项目信息传播更显著。凝聚性与地位差异对经验信息传播的影响差异不显著,地位差异比协调性对经验信息传播影响更显著。认知临近性会正向影响社群凝聚性与两种信息传播的关系,会负向影响社群地位差异与两种信息传播的关系,会负向影响协调性与两种信息传播的关系。     

空间集聚与网络嵌入对技术创新的非线性影响

新一代信息技术产业面临技术与市场的双重不确定性,提升产业技术创新能力、激励企业开展技术创新成为理论界和实践界关注的焦点。以上海1966家云计算企业为样本,从地理空间集聚和社交网络嵌入两个维度,运用零膨胀负二项回归模型研究企业外部因素对技术创新的非线性影响。研究发现：空间集聚与企业技术创新间呈现U型关系,结合上海云计算产业城市空间分布情况可知,空间集聚对上海云计算创新的正向激励效应目前尚未显现;此外,以董事兼任构建公司社交网络,发现在环境不确定性较高的云计算市场,网络中心度对企业创新呈现一致性的正向影响。研究结论对云计算产业技术创新激励具有启示作用。     

信任不对称与网络嵌入对团队创新机制的影响

创新团队中,成员的交互记忆系统反映了团队成员基于对彼此掌握的知识的认知而形成的分工协作机制,对于团队的创新绩效具有重要影响。而团队成员之间的信任关系与网络嵌入特征则是构成了影响交互记忆系统形成与发展的关键因素。本文在探讨交互记忆系统对创新团队绩效作用效果的基础上,重点分析了信任不对称对交互记忆系统的影响机制,并考虑了以团队及成员的网络嵌入特征表现出的在其中的边界效应。本文选择某高校学生组成的移动应用软件项目设计团队开展实验研究。研究发现：首先,交互记忆系统对创新团队绩效具有显著的积极影响,其作用机制可归纳为团队创造力提升、任务效率改善提高和成员满意度改善三条路径;其次,信任不对称不利于交互记忆系统的形成与发展。信任不对称会触发团队成员的知识隐藏行为,不利于对彼此知识的识别、检索和协调,阻碍交互记忆系统的有效运行;再者,成员之间的社会网络具有显著的边界效应。网络中心性能够显著削弱信任不对称对交互记忆系统的抑制作用,但同时降低了交互记忆系统对团队绩效的积极贡献。本文研究结论不仅能够从交互记忆系统视角拓展了创新团队绩效驱动机制的相关研究,还有利于从网络嵌入视角揭示了交互记忆系统驱动创新团队绩效的边界条件,对于提升团队和企业创新绩效具有重要的实践意义。     

全球价值链嵌入与我国制造业技术含量提升

本文利用世界投入产出数据库（WIOD）的数据,分别测算了中国制造业总体及16个细分行业的全球价值链嵌入程度以及国内技术含量,并实证分析了全球价值链嵌入对国内技术含量的影响。研究发现,全球价值链嵌入能够有效促进国内技术含量提升,并且全球价值链嵌入与国内技术含量存在非线性的倒U型关系。全球价值链嵌入通过中间产品效应、行业竞争效应以及大市场效应促进国内技术含量的提升。国内自主研发和国外技术引进都可以促进国内技术含量的提升,但国内自主研发对国内技术含量的作用要大于国外技术引进。从行业异质性来看,全球价值链嵌入对劳动密集型行业的促进作用要大于资本技术密集型行业以及高技术行业。     

基于社会网络视角的产业结构总体特征研究

针对当前产业结构研究的局限和不足,本文率先在该研究领域引入了社会网络的研究方法。通过社会网络与投入产出表的有机结合,提出了一种全新的、基于"关系"和"结构"视角的产业结构研究方法。利用我国2002年的投入产出数据和本文提出的研究方法,对我国产业结构的总体特征进行了实证研究。利用块模型分析方法,阐明了我国产业八大区块的范围和边界;刻画了我国产业总体结构的"中心化趋势",并对位于中心的产业进行了说明;提出了"最佳替代产业"和"自反性"产业区块的概念和实例,并分析了其在国家产业结构调整和优化过程中如何发挥各自的优势和作用。     

组织知识共享网络模型研究——基于知识网络和社会网络

 以组织中同时作为社会活动和知识活动载体的“人”为研究对象,以构建一个客观高效的组织知识共享网络模型为研究目的,提出基于知识网络和社会网络的组织知识共享网络模型及构建方法,并探讨该网络模型如何影响组织中主体间知识共享的过程和方式。从应用实例来看,该方法具有客观、可视化、定量等特点,因而可以较好地应用到组织知识的分布、查找、推送等知识共享实际应用中去,为组织知识共享的定量分析提供了一种新的工具和思路。     

高校社会资本影响组织创新、办学绩效的实证研究

摘要：本文以社会关系网络结构的视角,构建了高校社会资本对组织创新、教学与科研绩效产生影响的概念模型,并以我国69家高校作为调研对象进行了实证研究,通过多元回归分析和结构方程模型等方法检验了涵盖高校内外部社会网络的高校社会资本三维度的测量模型是可行有效的;证实了组织创新在高校社会资本影响教学与科研绩效过程中的中介作用;解析了内、外部社会资本的结构维、关系维、认知维对教学与科研绩效的直接和间接影响方面的效应差异及其交互作用。找到了高校关系维社会资本会对组织创新产生负效应的路径,论证了“强关系”和“过度信任”会产生负作用;但是综合高校内、外部社会资本各维度水平的交互总效应,它们对教学与科研绩效水平、组织创新能力的影响仍然是正向显著的。同时,本文也考察了“学校属性”、“办学历史”、“211工程”、“985工程”、“地理区域”作为控制变量对于高校教学与科研绩效的影响。     

网络社群结构动态对企业创新绩效的影响研究

基于社会网络理论、资源基础观和成长曲线模型,考察不同技术生命周期阶段下企业在创新网络中的社群内结构动态（群内稳定、群内扩张）和社群间结构动态（群间扩张）对企业创新绩效的影响。运用SDC数据库、USPTO、JPO、EPO以及样本企业所在国家专利数据库,以通信设备领域的联盟企业为样本进行实证研究,结果表明在技术导入期,企业群间扩张对创新绩效有负向影响;在技术成长期,企业群间扩张以及保持群内稳定对创新绩效有正向影响;在技术成熟期,企业群内稳定对创新绩效有正向影响,群内扩张与群间扩张对创新绩效有负向影响。在此基础上,提出不同生命周期阶段企业通过调整创新合作网络以提升企业创新绩效的政策建议。     

[2015年第2期] 在线社交网络的可视化分析

随着在线社交网络规模的指数性增长, 可视化分析成为理解社交媒体 及用户的重要手段。 文章针对社交媒体、社交用户 、社交媒体与用户的交互 3 个 角度, 从社交媒体主题可视化建模、社交用户偏好可视化建模、社交网络个性化 检索可视化 3 个方面,分析社交网络可视化技术的研究成果。 提出时空融合可视 化、“三网 ”融合可视化等未来国内研究方向。 以期利用在线社交网络, 为互联网 服务、国家安全提供有力支撑。     

网络嵌入对技术创业企业商业模式创新的影响

为解释网络嵌入对技术创业企业商业模式创新的作用机理,基于网络嵌入理论和新制度主义理论,以组织合法性为中介变量,构建了网络嵌入对技术创业企业商业模式创新影响的概念模型;根据409家企业的问卷调查数据,采用结构方程模型进行了实证检验。研究结果表明：1）结构嵌入和关系嵌入均对技术创业企业商业模式创新具有显著促进作用,其中关系嵌入对技术创业企业商业模式创新的作用更显著;2）组织合法性在结构嵌入和关系嵌入与商业模式创新关系间发挥着部分中介作用,提升组织合法性有助于技术创业企业通过网络嵌入促进商业模式创新。     

城市网络与创新能力间的时空关系研究 --以成渝城市群为例

摘要：以创新为动力,以城市群为载体是我国未来一段时期重要的经济发展战略。并且创新能力的持续提升也愈发受到城市网络的影响。据此,本文以正在发育的成渝城市群为研究对象,以城市间百度指数和联合专利为变量构建城市网络,并从创新产出、投入、环境三方面对创新能力进行测度,随后利用象限图法对二者间的相关性及其时空变化做出分析。最后,运用障碍度模型对成渝城市群创新能力进一步提升的相关阻碍因素进行识别,从而为日后该地区城市网络发展规划更好地服务于创新能力的提升提供政策建议。