Collaborator recommendation in heterogeneous bibliographic networks using random walks

The increasingly growing popularity of the collaboration among researchers and the increasing information overload in big scholarly data make it imperative to develop a collaborator recommendation system for researchers to find potential partners. Existing works always study this task as a link prediction problem in a homogeneous network with a single object type (i.e., author) and a single link type (i.e., co-authorship). However, a real-world academic social network often involves several object types, e.g., papers, terms, and venues, as well as multiple relationships among different objects. This paper proposes a RWR-CR (standing for random walk with restart-based collaborator recommendation) algorithm in a heterogeneous bibliographic network towards this problem. First, we construct a heterogeneous network with multiple types of nodes and links with a simplified network structure by removing the citing paper nodes. Then, two importance measures are used to weight edges in the network, which will bias a random walker’s behaviors. Finally, we employ a random walk with restart to retrieve relevant authors and output an ordered recommendation list in terms of ranking scores. Experimental results on DBLP and hep-th datasets demonstrate the effectiveness of our methodology and its promising performance in collaborator prediction.     

Disruptive coefficient and 2-step disruptive coefficient: Novel measures for identifying vital nodes in complex networks

The identification and ranking of vital nodes in complex networks have been a critical issue for a long time. In this paper, we present an extension of existing disruptive metrics and introduce new ones, namely the disruptive coefficient (D) and 2-step disruptive coefficient (2-step D), as innovative tools for identifying critical nodes in complex networks. Our approach emphasizes the importance of disruptiveness in characterizing nodes within the network and detecting their criticality. Our new measures take into account both prior and posterior information of the focal nodes, by evaluating their ability to disrupt the previous network paradigm, setting them apart from traditional measures. We conduct an empirical analysis of four real-world networks to compare the rankings or identification of nodes using D and 2stepD with those obtained from four renowned benchmark measures, namely, degree, h-index, PageRank, and the CD index. Our analysis reveals significant differences between the nodes identified by D and 2stepD and those identified by the benchmark measures. We also examine the correlation coefficient and efficiency of the metrics and find that D and 2stepD have significant correlations with the CD index, but have weak correlations with the benchmark measures. Furthermore, we show that D and 2stepD outperform CD index and random ways in intentional attacks. We find power law distributions for D, 2stepD, and CD, indicating a small number of highly disruptive nodes and a large number of less disruptive nodes in the networks. Our results suggest that D and 2stepD are capable of providing valuable and distinct insights for identifying critical nodes in complex networks.     

Effect of class imbalance in heterogeneous network embedding: An empirical study

Network science has been extensively explored in solving various bibliometrics tasks such as Co-authorship prediction, Author classification, Author clustering, Author ranking, Paper ranking, etc. While majority of the past studies exploit homogeneous bibliographic network (consists of singular type of nodes and edges), in recent past there is a surge in using heterogeneous bibliographic entities and their inter-dependencies using heterogeneous information networks (HIN). Unlike homogeneous bibliographic networks, a bibliographic HIN consists of multi-typed nodes such as Author, Paper, Venue, etc. and corresponding relations. Thus bibliographic HIN is more complex and captures rich semantics of underlying bibliographic data as well as poses more challenges. Since a real-world HIN may have different number of instances for different node types, class imbalance is ubiquitous. Recent studies discuss class imbalance in brief and exploit meta-path-based strategies to address the issue. However, there is no work which quantitatively study the effect of class imbalance in regards to solving real-world bibliometrics tasks. Therefore, this paper first proposes a metric to estimate class imbalance in HIN and study the effects of class imbalance over two bibliometrics tasks, namely (i) Co-authorship prediction and (ii) Author's research area classification, using node features generated by network embedding-based frameworks for DBLP dataset. From various experimental analysis, it is evident that class imbalance in bibliographic HIN is an inherent characteristic and for better performance of the above-mentioned bibliometrics tasks, the bibliographic HINs must consider Author, Paper, and Venue as node types.     

Quantifying the interdisciplinarity of scientific journals and fields

There is an overall perception of increased interdisciplinarity in science, but this is difficult to confirm quantitatively owing to the lack of adequate methods to evaluate subjective phenomena. This is no different from the difficulties in establishing quantitative relationships in human and social sciences. In this paper we quantified the interdisciplinarity of scientific journals and science fields by using an entropy measurement based on the diversity of the subject categories of journals citing a specific journal. The methodology consisted in building citation networks using the Journal Citation Reports^® database, in which the nodes were journals and edges were established based on citations among journals. The overall network for the 11-year period (1999–2009) studied was small-world and followed a power-law with exponential cutoff distribution with regard to the in-strength. Upon visualizing the network topology an overall structure of the various science fields could be inferred, especially their interconnections. We confirmed quantitatively that science fields are becoming increasingly interdisciplinary, with the degree of interdisplinarity (i.e. entropy) correlating strongly with the in-strength of journals and with the impact factor.     

Investigating relationships within and between category networks in Wikipedia

This work maps and analyses cross-citations in the areas of Biology, Mathematics, Physics and Medicine in the English version of Wikipedia, which are represented as an undirected complex network where the entries correspond to nodes and the citations among the entries are mapped as edges. We found a high value of clustering coefficient for the areas of Biology and Medicine, and a small value for Mathematics and Physics. The topological organization is also different for each network, including a modular structure for Biology and Medicine, a sparse structure for Mathematics and a dense core for Physics. The networks have degree distributions that can be approximated by a power-law with a cut-off. The assortativity of the isolated networks has also been investigated and the results indicate distinct patterns for each subject. We estimated the betweenness centrality of each node considering the full Wikipedia network, which contains the nodes of the four subjects and the edges between them. In addition, the average shortest path length between the subjects revealed a close relationship between the subjects of Biology and Physics, and also between Medicine and Physics. Our results indicate that the analysis of the full Wikipedia network cannot predict the behavior of the isolated categories since their properties can be very different from those observed in the full network.     

An improved SAO network-based method for technology trend analysis: A case study of graphene

This paper proposes an improved Subject-Action-Object (SAO) network-based method for analyzing trends in technology development. It attempts to address shortcomings of the traditional SAO network approach, i.e., when setting Subject, Action and Object as nodes of the network, there may be errors in explaining the relationship between Subject Node and Object Node, and the strength of the relationship between subject and object also cannot be identified. The proposed improved SAO network-based method in this paper includes: (1) a new method for constructing an SAO network based on SAO links that calculate the intensity of the relationship between nodes; (2) a model for identifying technology development trends based on structural holes, changes in the distribution of node degrees, and shifts in network centrality. An empirical study on graphene technology is used to illustrate the validity and feasibility of the proposed method.     

Social network analysis: An approach and technique for the study of information exchange

Social network analysis is an approach and set of techniques used to study the exchange of resources among actors (i.e., individuals, groups, or organizations). One such resource is information. Regular patterns of information exchange reveal themselves as social networks, with actors as nodes in the network and information exchange relationships as connectors between nodes. Just as roads structure the flow of resources among cities, information exchange relationships structure the flow of information among actors. Social network analysis assesses information opportunities for individuals or groups of individuals in terms of exposure to and control of information. By gaining awareness of existing information exchange routes, information providers can act on information opportunities and make changes to information routes to improve the delivery of information services.     

Three-feature model to reproduce the topology of citation networks and the effects from authors’ visibility on their h-index

Various factors are believed to govern the selection of references in citation networks, but a precise, quantitative determination of their importance has remained elusive. In this paper, we show that three factors can account for the referencing pattern of citation networks for two topics, namely “graphenes” and “complex networks”, thus allowing one to reproduce the topological features of the networks built with papers being the nodes and the edges established by citations. The most relevant factor was content similarity, while the other two – in-degree (i.e. citation counts) and age of publication – had varying importance depending on the topic studied. This dependence indicates that additional factors could play a role. Indeed, by intuition one should expect the reputation (or visibility) of authors and/or institutions to affect the referencing pattern, and this is only indirectly considered via the in-degree that should correlate with such reputation. Because information on reputation is not readily available, we simulated its effect on artificial citation networks considering two communities with distinct fitness (visibility) parameters. One community was assumed to have twice the fitness value of the other, which amounts to a double probability for a paper being cited. While the h-index for authors in the community with larger fitness evolved with time with slightly higher values than for the control network (no fitness considered), a drastic effect was noted for the community with smaller fitness.     

Measuring social capital through network analysis and its influence on individual performance

Studies of social networks highlight the importance of network structure or structural properties of a given network and its impact on performance outcome. One of the important properties of this network structure is referred to as social capital, which is the network of contacts and the associated values attached to these networks of contacts. This study provides empirical evidence of the influence of social capital and performance within the context of academic collaboration (coauthorship) and suggests that the collaborative process involves social capital embedded within relationships and network structures among direct coauthors. Association between scholars' social capital and their citation-based performance measures is examined. To overcome the limitations of traditional social network metrics for measuring the influence of scholars' social capital within coauthorship networks, the traditional social network metrics is extended by proposing two new measures, of which one is non-weighted (the power–diversity index) and the other (power–tie–diversity index) is weighted by the number of collaboration instances. The Spearman's correlation rank test is used to examine the association between scholars' social capital measures and their citation-based performance. Results suggest that research performance of authors is positively correlated with their social capital measures. The power–diversity index and power–tie–diversity index serve as indicators of power and influence of an individual's ability to control communication and information.     

Exposing multi-relational networks to single-relational network analysis algorithms

Many, if not most network analysis algorithms have been designed specifically for single-relational networks; that is, networks in which all edges are of the same type. For example, edges may either represent “friendship,” “kinship,” or “collaboration,” but not all of them together. In contrast, a multi-relational network is a network with a heterogeneous set of edge labels which can represent relationships of various types in a single data structure. While multi-relational networks are more expressive in terms of the variety of relationships they can capture, there is a need for a general framework for transferring the many single-relational network analysis algorithms to the multi-relational domain. It is not sufficient to execute a single-relational network analysis algorithm on a multi-relational network by simply ignoring edge labels. This article presents an algebra for mapping multi-relational networks to single-relational networks, thereby exposing them to single-relational network analysis algorithms.     

基于表示学习的双层知识网络链路预测

当前,针对知识网络的链路预测主要是基于网络拓扑结构的相似性,很少考虑作者的研究领域,导致信息利用不充分等问题,因此本文提出了双层知识网络的链路预测框架hypernet2vec。双层知识网络,即作者合著关系网络和学术领域关系网络,利用网络表示学习,分别将两层网络中的节点映射到低维的向量空间,再输入到专门设计的卷积神经网络中计算并进行链路预测。与经典的链路预测指标如RA指标、LP指标和LRW指标等相比,hypernet2vec模型预测的AUC(area under curve)值取得了显著的提升,平均提升幅度达11.17%。文章还从情报产生层面和复杂系统层面,对模型发生作用的深层机理进行了探讨。     

Inoculation and Mental Processing: The Instrumental Role of Associative Networks in the Process of Resistance to Counterattitudinal Influence

This investigation introduced the concept of associative networks to the resistance domain. A four-phase experiment was conducted involving 298 participants. The results confirmed the role of known elements in the process of resistance: the core elements of threat, issue involvement, and counterarguing output facilitated resistance, as did the more recently introduced element of attitude certainty. Also, the results indicated that inoculation treatments modified the structure of associative networks, planting additional nodes and increasing the linkages between nodes. Subsequently, changes in the structure of associative networks contributed to resistance to counterattitudinal attacks.     

Informative Radio Models in Brazil: The All-News Networks

This study analyzes the particularities of Brazilian radio networks that adopt the all-news format and briefly presents the main national and international experiences for the implementation of the model and its conceptualization. Besides the bibliographic review, we use the multiple-case study, analyzing as the empirical objects CBN and BandNews FM networks. Also, we apply methodological procedures of systematic non-participant observation, supplemented by interviews and surveys. We conclude that the different all-news programming models and network organizations influence the processes of production, information structure, broadcasting language, and therefore the stations' profile.     

领域知识聚类性的动态演化分析

[目的/意义]探索领域知识发展过程中的聚类演化问题有助于揭示知识聚类的特征和规律,对于掌握知识生长演进过程中关联知识的聚集具有重要意义。[方法/过程]以复杂网络的思想为基础,基于标签邻接关系的发生值构建时间序列领域知识网络。即依据网络模体的理论,采用网络聚类系数的分析方法,对领域知识网络进行动态跟踪与分析;结合网络密度、特征路径长度、节点度值、封闭三元组等指标,从随机因素、度相关性、邻近关联3个方面对领域知识发展过程中的聚类演化现象进行分析。[结果/结论]研究结果表明：①领域知识在发展进程中始终保持较高的聚类性;②领域知识的聚类性同时包含随机性与结构性（非随机性）两方面因素; ③领域知识聚类的动态状态在小世界网络和无标度网络之间摇摆演化; ④领域知识的聚类状态在网络全局和局部节点之间表现出一定的差异性。     

A layered framework to study collaboration as a form of knowledge sharing and diffusion

Collaboration can be described using layered systems such as the article–author–institute–country structure. These structures can be considered ‘cascades’ or ‘chains’ of bipartite networks. We introduce a framework for characterizing and studying the intensity of collaboration between entities at a given level (e.g., between institutions). Specifically, we define the notions of significant, essential and vital nodes, and significant, essential and vital sub paths to describe the spread of knowledge through collaboration in such systems. Based on these notions, we introduce relative and absolute proper essential node (PEN) centrality as indicators of a node's importance for diffusion of knowledge through collaboration.We illustrate these concepts in an illustrative example and show how they can be applied using a small real-world example. Since collaboration implies knowledge sharing, it can be considered a special form of knowledge diffusion.     

Convexity in scientific collaboration networks

Convexity in a network (graph) has been recently defined as a property of each of its subgraphs to include all shortest paths between the nodes of that subgraph. It can be measured on the scale [0, 1] with 1 being assigned to fully convex networks. The largest convex component of a graph that emerges after the removal of the least number of edges is called a convex skeleton. It is basically a tree of cliques, which has been shown to have many interesting features. In this article the notions of convexity and convex skeletons in the context of scientific collaboration networks are discussed. More specifically, we analyze the co-authorship networks of Slovenian researchers in computer science, physics, sociology, mathematics, and economics and extract convex skeletons from them. We then compare these convex skeletons with the residual graphs (remainders) in terms of collaboration frequency distributions by various parameters such as the publication year and type, co-authors’ birth year, status, gender, discipline, etc. We also show the top-ranked scientists by four basic centrality measures as calculated on the original networks and their skeletons and conclude that convex skeletons may help detect influential scholars that are hardly identifiable in the original collaboration network. As their inherent feature, convex skeletons retain the properties of collaboration networks. These include high-level structural properties but also the fact that the same authors are highlighted by centrality measures. Moreover, the most important ties and thus the most important collaborations are retained in the skeletons.     

网络信息安全人因失误发现及其纠正框架

网络信息安全人因失误发现是人因失误分析及其纠正的起点,人因失误发现的重要途径是通过4个方面的比较实现：预计效果与实现结果;设备失效与自身的失误效果;计划行为与执行行为;意图与计划,从它们之间的失配,发现信息安全人因失误。论文提出一种网络信息安全人因失误纠正框架,主要从人因失误分析与确定、纠正计划及其实施三个方面展开。失误分析与确定是通过调查研究,对人因失误原因进行说明与辨识;通过构建纠正框架,期望及时发现并纠正人因失误,从而有效控制并相应减少人因失误的发生。     

跨地域科研协作模式分析

在系统调研跨地域科研协作现状基础上,本研究提出跨地域科研协作模式分析框架,以信息搜寻与信息检索融合（IS&R）等为测试主题,构建跨地域科研协作网络;计算无向加权科研协作网络节点中心性,发现各主题研究热点国家、城市和机构;模拟有向加权科研协作网络连接强度,描绘科研协作关系中知识流动方向;识别科研协作过程中节点角色,发掘城市科研协作主流模式;通过QAP分析,测度地理距离对节点间科研协作强度的影响,剖析节点科研实力与节点间科研协作强度的相关关系;借助演化分析,厘清科研协作网络发展历程及节点角色迁移情况。结果显示,上述主题在跨地域科研协作过程中既存在共性的节点分布、网络连接和扩展模式,又表现出一定的学科差异。图5。表11。参考文献23。     

图书馆图书借阅系统与单标度二元网络模型

本文从网络的角度 ,研究了图书馆这样一种有趣的复杂系统。读者和图书之间通过借阅建立联系 ,可以在两个层次上用网络语言来描述 ,即二元 (读者—图书 )和单元 (读者—读者 ,图书—图书 )网络。我们以研究配位数分布为工具 ,研究了北京师范大学图书馆外借处图书在 14个月内的借阅情况所构成的网络 ,发现其体现了很好的单标度性质 ,即配位数分布体现为一指数衰减的形式。随后提出了一个单标度二元网络模型 ,对此进行解释 ,定性地重现了这一实测结果。     

一个新的信息传播模型及其模拟

信息传播过程包含两个因素：一是信息传播的规则,描述人如何接受和传播信息;二是信息传播所在网络的结构。通过构建包含这两个因素的信息传播模型,并在计算机上模拟,得出如下结论：首先,存在信息传播临界值。当信息吸引力大于临界值时信息才能传播开,否则几乎不能传播。其次,小世界网络上,当信息吸引力在临界值附近时,随机边的添加会使传播面积增大,从而导致实际的传播临界值减小;但当信息吸引力足够大时,图结构的变化对信息传播面积影响不大。第三,当信息吸引力在临界值附近时,信息传播耗时对网络规模很敏感,但随着信息吸引力的增加,其值趋于稳定。