Online Social Networks have embarked on the importance of connection strength measures which has a broad array of applications such as, analyzing diffusion behaviors, community detection, link predictions, recommender systems. Though there are some existing connection strength measures, the density that a connection shares with it's neighbors and the directionality aspect has not received much attention. In this paper, we have proposed an asymmetric edge similarity measure namely, Neighborhood Density-based Edge Similarity (NDES) which provides a fundamental support to derive the strength of connection. The time complexity of NDES is $O(nk^2)$. An application of NDES for community detection in social network is shown. We have considered a similarity based community detection technique and substituted its similarity measure with NDES. The performance of NDES is evaluated on several small real-world datasets in terms of the effectiveness in detecting communities and compared with three widely used similarity measures. Empirical results show NDES enables detecting comparatively better communities both in terms of accuracy and quality.

Community detection in Social Networks is associated with finding and grouping the most similar nodes inherent in the network. These similar nodes are identified by computing tie strength. Stronger ties indicates higher proximity shared by connected node pairs. This work is motivated by Granovetter's argument that suggests that strong ties lies within densely connected nodes and the theory that community cores in real-world networks are densely connected. In this paper, we have introduced a novel method called \emph{Disjoint Community detection using Cascades (DCC)} which demonstrates the effectiveness of a new local density based tie strength measure on detecting communities. Here, tie strength is utilized to decide the paths followed for propagating information. The idea is to crawl through the tuple information of cascades towards the community core guided by increasing tie strength. Considering the cascade generation step, a novel preferential membership method has been developed to assign community labels to unassigned nodes. The efficacy of $DCC$ has been analyzed based on quality and accuracy on several real-world datasets and baseline community detection algorithms.

Information diffusion in Online Social Networks is a new and crucial problem in social network analysis field and requires significant research attention. Efficient diffusion of information are of critical importance in diverse situations such as; pandemic prevention, advertising, marketing etc. Although several mathematical models have been developed till date, but previous works lacked systematic analysis and exploration of the influence of neighborhood for information diffusion. In this paper, we have proposed Common Neighborhood Strategy (CNS) algorithm for information diffusion that demonstrates the role of common neighborhood in information propagation throughout the network. The performance of CNS algorithm is evaluated on several real-world datasets in terms of diffusion speed and diffusion outspread and compared with several widely used information diffusion models. Empirical results show CNS algorithm enables better information diffusion both in terms of diffusion speed and diffusion outspread.

通常，通过比较使用不同算法获得的社区的评估度量值来评估社区检测算法。用于衡量社区质量的评估指标结合了实体的拓扑信息，例如社区内部或外部节点的连接性。但是，在比较度量值的同时，它失去了社区拓扑信息在比较过程中的直接参与。在本文中，提出了一种直接比较方法，直接比较了两种算法获得的社区的拓扑信息。质量度量是基于社区拓扑信息的直接比较而设计的。考虑到新设计的质量度量，开发了两个排名方案。研究了八种广泛使用的现实世界数据集和六种社区检测算法的拟议质量指标以及排名方案的功效。

复杂的网络是代表现实生活系统的图形，这些系统表现出独特的特征，这些特征在纯粹的常规或完全随机的图中未发现。由于基础过程的复杂性，对此类系统的研究至关重要，但具有挑战性。然而，由于大量网络数据的可用性，近几十年来，这项任务变得更加容易。复杂网络中的链接预测旨在估计网络中缺少两个节点之间的链接的可能性。由于数据收集的不完美或仅仅是因为它们尚未出现，因此可能会缺少链接。发现网络数据中实体之间的新关系吸引了研究人员在社会学，计算机科学，物理学和生物学等各个领域的关注。大多数现有研究的重点是无向复杂网络中的链接预测。但是，并非所有现实生活中的系统都可以忠实地表示为无向网络。当使用链接预测算法时，通常会做出这种简化的假设，但不可避免地会导致有关节点之间关系和预测性能中降解的信息的丢失。本文介绍了针对有向网络的明确设计的链接预测方法。它基于相似性范式，该范式最近已证明在无向网络中成功。提出的算法通过在相似性和受欢迎程度上将其建模为不对称性来处理节点关系中的不对称性。鉴于观察到的网络拓扑结构，该算法将隐藏的相似性近似为最短路径距离，并使用边缘权重捕获并取消链接的不对称性和节点的受欢迎程度。在现实生活中评估了所提出的方法，实验结果证明了其在预测各种网络数据类型和大小的丢失链接方面的有效性。

Most real-world networks suffer from incompleteness or incorrectness, which is an inherent attribute to real-world datasets. As a consequence, those downstream machine learning tasks in complex network like community detection methods may yield less satisfactory results, i.e., a proper preprocessing measure is required here. To address this issue, in this paper, we design a new community attribute based link prediction strategy HAP and propose a two-step community enhancement algorithm with automatic evolution process based on HAP. This paper aims at providing a community enhancement measure through adding links to clarify ambiguous community structures. The HAP method takes the neighbourhood uncertainty and Shannon entropy to identify boundary nodes, and establishes links by considering the nodes' community attributes and community size at the same time. The experimental results on twelve real-world datasets with ground truth community indicate that the proposed link prediction method outperforms other baseline methods and the enhancement of community follows the expected evolution process.

Clustering is a fundamental problem in network analysis that finds closely connected groups of nodes and separates them from other nodes in the graph, while link prediction is to predict whether two nodes in a network are likely to have a link. The definition of both naturally determines that clustering must play a positive role in obtaining accurate link prediction tasks. Yet researchers have long ignored or used inappropriate ways to undermine this positive relationship. In this article, We construct a simple but efficient clustering-driven link prediction framework(ClusterLP), with the goal of directly exploiting the cluster structures to obtain connections between nodes as accurately as possible in both undirected graphs and directed graphs. Specifically, we propose that it is easier to establish links between nodes with similar representation vectors and cluster tendencies in undirected graphs, while nodes in a directed graphs can more easily point to nodes similar to their representation vectors and have greater influence in their own cluster. We customized the implementation of ClusterLP for undirected and directed graphs, respectively, and the experimental results using multiple real-world networks on the link prediction task showed that our models is highly competitive with existing baseline models. The code implementation of ClusterLP and baselines we use are available at https://github.com/ZINUX1998/ClusterLP.

社交网络（SN）是一个由代表它们之间相互作用的群体组成的社会结构。 SNS最近被广泛使用，随后已成为产品推广和信息扩散的合适平台。 SN中的人们直接影响彼此的利益和行为。 SNS中最重要的问题之一是，如果选择将它们作为网络扩散场景的种子节点选择，那么他们可以以级联的方式对网络中的其他节点产生最大影响。有影响力的扩散器是人们，如果他们被选为网络中出版问题中的种子，那么该网络将拥有最多了解该扩散实体的人。这是称为影响最大化（IM）问题的文献中的一个众所周知的问题。尽管已证明这是一个NP完整的问题，并且在多项式时间内没有解决方案，但有人认为它具有子模块化功能的属性，因此可以使用贪婪的算法来解决。提出改善这种复杂性的大多数方法都是基于以下假设：整个图都是可见的。但是，此假设不适合许多真实世界图。进行了这项研究，以扩展使用链接预测技术与伪可见性图的电流最大化方法。为此，将一种称为指数随机图模型（ERGM）的图生成方法用于链接预测。使用斯坦福大学SNAP数据集的数据对所提出的方法进行了测试。根据实验测试，所提出的方法在现实世界图上有效。

时间网络链接预测是网络科学领域的重要任务，并且在实际情况下具有广泛的应用。揭示网络的进化机制对于链接预测至关重要，如何有效利用历史信息来实现时间链接并有效提取网络结构的高阶模式仍然是一个至关重要的挑战。为了解决这些问题，在本文中，我们提出了一个具有调整后的Sigmoid函数和2-Simplex结构（TLPSS）的新型时间链接预测模型。调整后的Sigmoid衰减模式考虑了活跃，衰减和稳定的边缘状态，这适当适合信息的生命周期。此外，引入了由单纯形高阶结构组成的潜在矩阵序列，以增强链接预测方法的性能，因为它在稀疏网络中非常可行。结合信息的生命周期和单纯级结构，通过满足动态网络中时间和结构信息的一致性来实现TLPS的整体性能。六个现实世界数据集的实验结果证明了TLPS的有效性，与其他基线方法相比，我们提出的模型平均提高了链接预测的性能15％。

隐藏的社区是最近提出的一个有用的概念，用于社交网络分析。为了处理网络规模的快速增长，在这项工作中，我们从本地角度探讨了隐藏社区的检测，并提出了一种在从原始网络采样的子程目上迭代地检测和提升每个层的新方法。我们首先将根据我们修改的本地频谱方法从单个种子节点展开种子集，并检测初始占主导地位的本地社区。然后，我们暂时删除该社区的成员以及它们与其他节点的连接，并检测剩余子图中的所有邻居社区，包括一些“破坏社区”，该部分仅包含原始网络中的一部分成员。当地社区和邻里社区形成了一个主导层，通过减少这些社区内的边缘权重，我们削弱了这一层的结构来揭示隐藏的层。最终，我们重复整个过程，并且可以迭代地检测并升级包含种子节点的所有社区。理论上我们展示了我们的方法可以避免破碎的社区和当地社区被认为是子图中的一个社区的某些情况，导致对全球隐藏的社区检测方法可能引起的检测不准确。广泛的实验表明，我们的方法可以显着优于为全球隐藏社区检测或多个本地社区检测设计的最先进的基线。

Nature-inspired optimization Algorithms (NIOAs) are nowadays a popular choice for community detection in social networks. Community detection problem in social network is treated as optimization problem, where the objective is to either maximize the connection within the community or minimize connections between the communities. To apply NIOAs, either of the two, or both objectives are explored. Since NIOAs mostly exploit randomness in their strategies, it is necessary to analyze their performance for specific applications. In this paper, NIOAs are analyzed on the community detection problem. A direct comparison approach is followed to perform pairwise comparison of NIOAs. The performance is measured in terms of five scores designed based on prasatul matrix and also with average isolability. Three widely used real-world social networks and four NIOAs are considered for analyzing the quality of communities generated by NIOAs.

图表上的表示学习（也称为图形嵌入）显示了其对一系列机器学习应用程序（例如分类，预测和建议）的重大影响。但是，现有的工作在很大程度上忽略了现代应用程序中图和边缘的属性（或属性）中包含的丰富信息，例如，属性图表示的节点和边缘。迄今为止，大多数现有的图形嵌入方法要么仅关注具有图形拓扑的普通图，要么仅考虑节点上的属性。我们提出了PGE，这是一个图形表示学习框架，该框架将节点和边缘属性都包含到图形嵌入过程中。 PGE使用节点聚类来分配偏差来区分节点的邻居，并利用多个数据驱动的矩阵来汇总基于偏置策略采样的邻居的属性信息。 PGE采用了流行的邻里聚合归纳模型。我们通过显示PGE如何实现更好的嵌入结果的详细分析，并验证PGE的性能，而不是最新的嵌入方法嵌入方法在基准应用程序上的嵌入方法，例如节点分类和对现实世界中的链接预测数据集。

许多复杂网络的结构包括其拓扑顶部的边缘方向性和权重。可以无缝考虑这些属性组合的网络分析是可取的。在本文中，我们研究了两个重要的这样的网络分析技术，即中心和聚类。采用信息流基于集群的模型，该模型本身就是在计算中心的信息定理措施时构建。我们的主要捐款包括马尔可夫熵中心的广义模型，灵活地调整节点度，边缘权重和方向的重要性，具有闭合形式的渐近分析。它导致一种新颖的两级图形聚类算法。中心分析有助于推理我们对给定图形的方法的适用性，并确定探索当地社区结构的“查询”节点，从而导致群集聚类机制。熵中心计算由我们的聚类算法摊销，使其计算得高效：与使用马尔可夫熵中心为聚类的先前方法相比，我们的实验表明了多个速度的速度。我们的聚类算法自然地继承了适应边缘方向性的灵活性，以及​​边缘权重和节点度之间的不同解释和相互作用。总的来说，本文不仅具有显着的理论和概念贡献，还转化为实际相关性的文物，产生新的，有效和可扩展的中心计算和图形聚类算法，其有效通过广泛的基准测试进行了验证。

图形嵌入，代表数值向量的本地和全局邻域信息，是广泛的现实系统数学建模的关键部分。在嵌入算法中，事实证明，基于步行的随机算法非常成功。这些算法通过创建许多随机步行，并重新定义步骤来收集信息。创建随机步行是嵌入过程中最苛刻的部分。计算需求随着网络的规模而增加。此外，对于现实世界网络，考虑到相同基础上的所有节点，低度节点的丰度都会造成不平衡的数据问题。在这项工作中，提出了一种计算较少且节点连接性统一抽样方法。在提出的方法中，随机步行的数量与节点的程度成比例地创建。当将算法应用于大图时，所提出的算法的优点将变得更加增强。提出了使用两个网络（即Cora和Citeseer）进行比较研究。与固定数量的步行情况相比，提出的方法需要减少50％的计算工作，以达到节点分类和链接预测计算的相同精度。

图嵌入方法旨在通过将节点映射到低维矢量空间来查找有用的图表。这是一项具有重要下游应用程序的任务，例如链接预测，图形重建，数据可视化，节点分类和语言建模。近年来，图形嵌入领域见证了从线性代数方法转向基于局部的优化方法，结合了随机步行和深神经网络，以解决嵌入大图的问题。但是，尽管优化工具有所改进，但图形嵌入方法仍然是一般设计的，以忽略现实生活网络的特殊性的方式。确实，近年来，理解和建模复杂的现实生活网络取得了重大进展。但是，获得的结果对嵌入算法的发展产生了很小的影响。本文旨在通过设计一种图形嵌入方法来解决此问题，该方法利用网络科学领域的最新有价值的见解。更确切地说，我们基于普及性相似性和局部吸引力范例提出了一种新颖的图形嵌入方法。我们在大量现实生活网络上评估了在链接预测任务上提出的方法的性能。我们使用广泛的实验分析表明，所提出的方法优于嵌入算法的最先进的图。我们还证明了它对数据稀缺性和嵌入维度的选择的稳健性。

复杂网络分析的最新进展为不同领域的应用开辟了广泛的可能性。网络分析的功能取决于节点特征。基于拓扑的节点特征是对局部和全局空间关系和节点连接结构的实现。因此，收集有关节点特征的正确信息和相邻节点的连接结构在复杂网络分析中在节点分类和链接预测中起着最突出的作用。目前的工作介绍了一种新的特征抽象方法，即基于嵌入匿名随机步行向量上的匿名随机步行，即过渡概率矩阵（TPM）。节点特征向量由从预定义半径中的一组步行中获得的过渡概率组成。过渡概率与局部连接结构直接相关，因此正确嵌入到特征向量上。在节点识别/分类中测试了建议的嵌入方法的成功，并在三个常用的现实世界网络上进行了链接预测。在现实世界网络中，具有相似连接结构的节点很常见。因此，从类似网络中获取新网络预测的信息是一种显着特征，它使所提出的算法在跨网络概括任务方面优于最先进的算法。

局部内在维度（LID）的概念是数据维度分析的重要进步，并在数据挖掘，机器学习和相似性搜索问题中应用了。现有的基于距离的盖估计器设计用于包含欧几里得空间中向量的数据点的表格数据集。在讨论了考虑图嵌入和图形距离的图形结构数据的局限性之后，我们提出了NC-lid，这是一种与盖子相关的新型措施，用于量化最短路径距离相对于自然群落的固有区域的歧视能力。它显示了如何使用该度量来设计嵌入算法的图形图，并通过根据NC-LID值调整了Node2VEC的两个LID弹性变体。我们对NC-LID对大量实际图表的经验分析表明，该措施能够指向Node2VEC嵌入中具有高链路重建错误的节点，而不是节点中心度指标。实验评估还表明，通过在生成的嵌入中更好地保​​留图形结构，提出的盖 - 弹性节点2VEC扩展可以改善节点2VEC。

Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the linkprediction problem, and we develop approaches to link prediction based on measures for analyzing the "proximity" of nodes in a network. Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.

Graph embedding algorithms embed a graph into a vector space where the structure and the inherent properties of the graph are preserved. The existing graph embedding methods cannot preserve the asymmetric transitivity well, which is a critical property of directed graphs. Asymmetric transitivity depicts the correlation among directed edges, that is, if there is a directed path from u to v, then there is likely a directed edge from u to v. Asymmetric transitivity can help in capturing structures of graphs and recovering from partially observed graphs. To tackle this challenge, we propose the idea of preserving asymmetric transitivity by approximating high-order proximity which are based on asymmetric transitivity. In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity. More specifically, we first derive a general formulation that cover multiple popular highorder proximity measurements, then propose a scalable embedding algorithm to approximate the high-order proximity measurements based on their general formulation. Moreover, we provide a theoretical upper bound on the RMSE (Root Mean Squared Error) of the approximation. Our empirical experiments on a synthetic dataset and three realworld datasets demonstrate that HOPE can approximate the high-order proximities significantly better than the state-ofart algorithms and outperform the state-of-art algorithms in tasks of reconstruction, link prediction and vertex recommendation.

Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. With the recent advances in deep learning, graph neural networks have been used for link prediction and have achieved state-of-the-art performance. Nevertheless, existing methods developed for this purpose are typically discriminative, computing features of local subgraphs around two neighboring nodes and predicting potential links between them from the perspective of subgraph classification. In this formalism, the selection of enclosing subgraphs and heuristic structural features for subgraph classification significantly affects the performance of the methods. To overcome this limitation, this paper proposes a novel and radically different link prediction algorithm based on the network reconstruction theory, called GraphLP. Instead of sampling positive and negative links and heuristically computing the features of their enclosing subgraphs, GraphLP utilizes the feature learning ability of deep-learning models to automatically extract the structural patterns of graphs for link prediction under the assumption that real-world graphs are not locally isolated. Moreover, GraphLP explores high-order connectivity patterns to utilize the hierarchical organizational structures of graphs for link prediction. Our experimental results on all common benchmark datasets from different applications demonstrate that the proposed method consistently outperforms other state-of-the-art methods. Unlike the discriminative neural network models used for link prediction, GraphLP is generative, which provides a new paradigm for neural-network-based link prediction.