Toward online node classification on streaming networks

The proliferation of networked data in various disciplines motivates a surge of research interests on network or graph mining. Among them, node classification is a typical learning task that focuses on exploiting the node interactions to infer the missing labels of unlabeled nodes in the network. A vast majority of existing node classification algorithms overwhelmingly focus on static networks and they assume the whole network structure is readily available before performing learning algorithms. However, it is not the case in many real-world scenarios where new nodes and new links are continuously being added in the network. Considering the streaming nature of networks, we study how to perform online node classification on this kind of streaming networks (a.k.a. online learning on streaming networks). As the existence of noisy links may negatively affect the node classification performance, we first present an online network embedding algorithm to alleviate this problem by obtaining the embedding representation of new nodes on the fly. Then we feed the learned embedding representation into a novel online soft margin kernel learning algorithm to predict the node labels in a sequential manner. Theoretical analysis is presented to show the superiority of the proposed framework of online learning on streaming networks (OLSN). Extensive experiments on real-world networks further demonstrate the effectiveness and efficiency of the proposed OLSN framework.     

基于元图卷积的异质网络嵌入学习算法

异质网络嵌入是将异质网络中丰富的结构和语义信息嵌入到低维的节点表示中.图卷积网络是处理网络数据的一种有效方法,当前也被用于研究异质网络的多类型节点和多维关系的表示问题,现有的图卷积网络模型主要采用元路径来表示不同类型节点间的一种语义关系.然而,孤立的单条元路径无法准确地反映节点间的复杂语义,即不能充分利用节点间存在的多种高阶间接语义关系.针对上述问题,提出了一种基于元图卷积的异质网络嵌入学习算法MGCN(meta-graph convolutional network),包括基于元图的异构邻接矩阵计算以及学习节点的嵌入表示2个阶段,基于元图的异构邻接矩阵设计了融合多条元路径上的不同语义的计算方法,能够挖掘节点间的高阶间接关系,通过异构邻接矩阵的计算,能够聚合节点邻域特征为统一模式,此种卷积学习降低了图卷积方法的嵌入维数,从而减少了计算时间.在2个公开的异质网络数据集上进行社会计算基础研究任务的实验表明,MGCN在节点分类、聚类任务上比基线模型有更好的性能且需更少的训练时间.     

基于PPMI的异质属性网络嵌入

属性网络嵌入旨在映射网络中的节点和链接关系到低维空间,同时保留其固有的结构和属性特征.异质属性网络中多种类型的节点和链接关系给网络嵌入学习提供了丰富的辅助信息,同时也带来了新的挑战.提出异质属性网络嵌入模型(heterogeneous attribute network embedding based on the PPMI, HANEP),旨在将网络中多种类型的节点和(或)多种类型的链接关系映射到低维、紧凑的空间,同时保护节点的属性特征和不同类型对象之间的异质链接承载的复杂、多样且丰富的语义信息.HANEP模型首先基于样本属性的相似性构建属性图、依据元路径抽取异质属性网络的拓扑结构,然后通过随机冲浪获得属性和拓扑概率共现(probabilistic co-occurrence, PCO)矩阵,并计算其正点对互信息(positive point-wise mutual information, PPMI),进而采用多个自编码器(auto-encoder, AE)捕捉节点属性和异质链接的本质信息.元路径可以捕捉异质网络中多种类型节点间的链接关系,构建属性图可以清晰描述节点属性的非线性流行结构,属性和拓扑的局部成对约束和图表示有助于整合节点属性和网络拓扑的一致性和互补性关系,PPMI表示可以捕捉属性和拓扑的高阶近邻信息及潜在的复杂非线性关系.在3个真实数据集上的实验结果验证了HANEP算法的有效性.     

融合属性信息的异质网络嵌入方法

现有的异质网络嵌入方法不仅忽略了网络中的异质边及其对节点嵌入的不同影响,还未考虑到网络结构与节点属性的融合。为此提出了一种融合属性信息的异质网络嵌入方法（SHANE）。将序列到序列（seq2seq）模型应用到依据边类型划分的子图中,无缝融合节点的结构信息和属性信息,同时捕捉节点的高阶语义信息。实验表明,SHANE在两个不同类型的数据集中进行链接预测任务,可以取得相对显著的效果。     

Community detection with attributed random walk via seed replacement

Community detection methods based on random walks are widely adopted in various network analysis tasks. It could capture structures and attributed information while alleviating the issues of noises. Though random walks on plain networks have been studied before, in real-world networks, nodes are often not pure vertices, but own different characteristics, described by the rich set of data associated with them. These node attributes contain plentiful information that often complements the network, and bring opportunities to the random-walk-based analysis. However, node attributes make the node interactions more complicated and are heterogeneous with respect to topological structures. Accordingly, attributed community detection based on random walk is challenging as it requires joint modelling of graph structures and node attributes. To bridge this gap, we propose a Community detection with Attributed random walk via Seed replacement (CAS). Our model is able to conquer the limitation of directly utilize the original network topology and ignore the attribute information. In particular, the algorithm consists of four stages to better identify communities. (1) Select initial seed nodes in the network; (2) Capture the better-quality seed replacement path set; (3) Generate the structure-attribute interaction transition matrix and perform the colored random walk; (4) Utilize the parallel conductance to expand the communities. Experiments on synthetic and real-world networks demonstrate the effectiveness of CAS.     

A block-based generative model for attributed network embedding

Attributed network embedding has attracted plenty of interest in recent years. It aims to learn task-independent, low-dimensional, and continuous vectors for nodes preserving both topology and attribute information. Most of the existing methods, such as random-walk based methods and GCNs, mainly focus on the local information, i.e., the attributes of the neighbours. Thus, they have been well studied for assortative networks (i.e., networks with communities) but ignored disassortative networks (i.e., networks with multipartite, hubs, and hybrid structures), which are common in the real world. To model both assortative and disassortative networks, we propose a block-based generative model for attributed network embedding from a probability perspective. Specifically, the nodes are assigned to several blocks wherein the nodes in the same block share the similar linkage patterns. These patterns can define assortative networks containing communities or disassortative networks with the multipartite, hub, or any hybrid structures. To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block. We use a neural network to characterize the nonlinearity between node embeddings and node attributes. We perform extensive experiments on real-world and synthetic attributed networks. The results show that our proposed method consistently outperforms state-of-the-art embedding methods for both clustering and classification tasks, especially on disassortative networks.

     

Community Discovery Algorithm Based on Multi-Relationship Embedding

Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm.     

异质信息网络表征学习综述

随着信息技术的快速发展,信息网络无处不在,例如社交网络、学术网络、万维网等.由于网络规模不断扩大以及数据的稀疏性,信息网络的分析方法面临巨大挑战.作为应对网络规模及数据稀疏挑战的有效方法,信息网络表征学习旨在利用网络的拓扑结构、节点内容等信息将节点嵌入到低维的向量空间中,同时保留原始网络固有的结构特征和内容特征,从而使...     

异质信息网络高阶层次化嵌入学习与推荐预测

异质信息网络是一种异质数据表示形式,如何融合异质数据复杂语义信息,是推荐系统面临的挑战之一.利用弱关系具有的丰富语义和信息传递能力,构建一种面向推荐系统的异质信息网络高阶嵌入学习框架,主要包括:初始化信息嵌入、高阶信息嵌入聚合与推荐预测3个模块.初始化信息嵌入模块首先采用基于弱关系的异质信息网络最佳信任路径筛选算法,有效地避免在全关系异质信息网络中,采样固定数量邻居造成的信息损失,其次利用新定义的基于多头图注意力的多任务共享特征重要性度量因子,筛选出节点的语义信息,并结合交互结构,有效地表征网络节点;高阶信息嵌入聚合模块通过融入弱关系及网络嵌入对知识良好的表征能力,实现高阶信息表达,并利用异质信息网络的层级传播机制,将被采样节点的特征聚合到待预测节点;推荐预测模块利用高阶信息的影响力推荐方法,实现了推荐任务.该框架具有嵌入节点类型丰富、融合共享属性和隐式交互信息等特点.最后,实验验证UI-HEHo学习框架可有效地改善评级预测的准确性,以及推荐生成的针对性、新颖性和多样性,尤其是在数据稀疏的应用场景中,具有良好的推荐效果.     

异质信息网络中基于网络嵌入的影响力最大化

针对当前大部分影响力最大化算法忽略了异质信息网络包含多种节点类型和多种关系类型,且不同类型节点在原始空间无法直接度量的问题,提出了一种异质信息网络中基于网络嵌入的影响力最大化模型（influence maximization based on network embedding,IMNE）,用于选择初始扩散节点实现影响力最大化。该模型不仅可以在对异质信息网络进行编码的同时表征异质信息网络中潜在的信息,还可以捕获不同类型节点间影响力的不确定和复杂性。在3个真实数据集上的实验验证了IMNE算法的有效性。     

基于双视角的耦合网络表示学习算法

传统网络表示学习算法大多依赖于节点视角下的随机游走获取网络局部采样序列,再通过最大化相邻节点的共现概率将网络中的节点表示成低维向量.本文在真实网络上的经验分析表明,对节点和边两种视角分别进行随机游走会产生具有不同节点分布的采样序列,进而得到不同的社区划分.为此,本文提出了一种基于双视角的耦合表示学习算法DPBCNE.该方法基于边视角进行随机游走以获得不同于节点视角的采样结果,再融合基于节点视角下的节点采样序列进行耦合训练,以学习节点和边的表示.实验结果表明,相较于现有的网络表示学习算法,DPBCNE能更好地保留网络拓扑结构信息,并在下游分类和预测任务中获得更好的效果.     

基于复合关系图卷积的属性网络嵌入方法

网络嵌入的目的是学习网络中每个节点的低维稠密向量,该问题吸引了研究者的广泛关注.现有方法大多侧重于对图结构的建模,而忽略了属性信息.属性化网络嵌入方法虽然考虑了节点属性,但节点与属性之间的信息关系尚未得到充分的利用.提出了一种利用丰富的关系信息进行属性网络嵌入的新框架.为此,我们首先为属性网络构造节点及其属性之间的复合关系,随后提出一个复合关系图卷积网络(composite relation graph convolution network, CRGCN)模型对这2种网络中的复合关系进行编码.在真实世界的数据集上进行了广泛的实验,结果证明了该模型在多种社交网络分析的有效性.     

Unsupervised social network embedding via adaptive specific mappings

In this paper, we address the problem of unsuperised social network embedding, which aims to embed network nodes, including node attributes, into a latent low dimensional space. In recent methods, the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction performance. However, the non-linear property of node attributes and network structure is not efficiently fused in existing methods, which is potentially helpful in learning a better network embedding. To this end, in this paper, we propose a novel model called ASM (Adaptive Specific Mapping) based on encoder-decoder framework. In encoder, we use the kernel mapping to capture the non-linear property of both node attributes and network structure. In particular, we adopt two feature mapping functions, namely an untrainable function for node attributes and a trainable function for network structure. By the mapping functions, we obtain the low dimensional feature vectors for node attributes and network structure, respectively. Then, we design an attention layer to combine the learning of both feature vectors and adaptively learn the node embedding. In encoder, we adopt the component of reconstruction for the training process of learning node attributes and network structure. We conducted a set of experiments on seven real-world social network datasets. The experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.     

基于改进图注意机制的网络嵌入方法研究及应用

网络已被广泛用作抽象现实世界系统以及组织实体之间关系的数据结构;网络嵌入模型是将网络中的节点映射为连续向量空间表示的强大工具;基于图卷积(GCN,graph convolutional neural, GCN)的网络嵌入方法因受其模型迭代过程参数随机优化和聚合函数的影响,容易造成原始节点特征信息丢失的问题;为有效提升网络嵌入效果,针对于图神经网络模型在网络嵌入中节点表征学习的局限性,提出了一种基于二阶邻域基数保留策略的图注意力网络(SNCR-GAT,second-order neighborhood cardinality retention strategy graph attention network),通过聚合二阶邻域特征基数的方式,解决网络节点潜在特征学习过程中重要信息保留问题;通过在节点分类和可视化两个网络嵌入应用任务上进行实验,结果表明,SNCR-GAT模型在网络嵌入上的性能表现相比较基准方法更具优越性。     

融合双层注意力机制的属性网络节点嵌入

属性网络嵌入旨在学习网络中节点的低维表示,具有拓扑和属性相似的节点在嵌入空间彼此接近.注意力机制能有效学习网络中节点与其邻居的相对重要性并基于邻居重要性聚合节点表示.据此,提出一种在属性网络中融合双层注意力机制的节点嵌入算法NETA,可以有效地实现属性网络嵌入.该算法首先从拓扑结构捕获直接邻居,基于属性关系捕获间接邻居...     

基于图嵌入与支持向量机的社交网络节点分类方法

针对无属性社交网络的节点分类问题,提出了一种基于图嵌入与支持向量机,利用社交网络中节点之间关系特征,对节点进行分类的方法.首先,通过DeepWalk、LINE等多种图嵌入模型挖掘节点隐含关系特征的同时,将高维的社交网络数据转换为低维embedding向量.其次,提取节点度、聚集系数、PageRank值等特征信息,组合构成节点的特征向量.然后,利用支持向量机构建节点分类预测模型对节点进行分类预测.最后,在三个公开的社交网络数据集上实验,与对比方法相比,提出的方法在社交网络节点分类任务中能取得更好的分类效果.     

基于动态投影嵌入的多维异质网络可视化研究

异质网络是包含多种类型节点和边的复杂信息网络,因此异质网络的可视化通常涉及异质信息的有效处理与可视技术,传统的网络可视化技术对于异质网络可视化来说布局效果混乱、异质信息难以体现。为此提出一种基于动态投影嵌入的多维度异质网络可视化方法。该方法从异质网络的表示学习方法入手,提出动态投影嵌入模型来学习异质网络的节点表示,在此基础上,提出了多维度（空间）的可视化方法,将异质网络节点根据不同属性映射至不同关系空间中进行可视化分析,从而挖掘出潜在的语义信息。实验结果表明,提出的方法不仅使异质网络表示学习的评价指标（MRR）提升了10%,而且从多维度（空间）对异质网络进行可视化,有效地展示和挖掘了网络中的异质信息与潜在语义信息。     

DEM: Deep Entity Matching Across Heterogeneous Information Networks

Heterogeneous information networks, which consist of multi-typed vertices representing objects and multi-typed edges representing relations between objects, are ubiquitous in the real world. In this paper, we study the problem of entity matching for heterogeneous information networks based on distributed network embedding and multi-layer perceptron with a highway network, and we propose a new method named DEM short for Deep Entity Matching. In contrast to the traditional entity matching methods, DEM utilizes the multi-layer perceptron with a highway network to explore the hidden relations to improve the performance of matching. Importantly, we incorporate DEM with the network embedding methodology, enabling highly efficient computing in a vectorized manner. DEM’s generic modeling of both the network structure and the entity attributes enables it to model various heterogeneous information networks flexibly. To illustrate its functionality, we apply the DEM algorithm to two real-world entity matching applications: user linkage under the social network analysis scenario that predicts the same or matched users in different social platforms and record linkage that predicts the same or matched records in different citation networks. Extensive experiments on real-world datasets demonstrate DEM’s effectiveness and rationality.

     

基于深度自编码的局部增强属性网络表示学习

基于深度自编码器的网络表示,可以捕获高度非线性的网络结构,但当链接稀疏时学到的表示不够准确。针对这一问题,提出一种基于深度自编码的局部增强属性网络表示学习模型,以提高表示学习的准确度。该模型首先利用链接与属性特征,采用多个深度自编码器,学习保持网络拓扑结构及属性特征的低维网络表示。之后,基于节点间近邻结构及属性相似性,对学出的低维网络表示进行节点约束,实现网络局部结构增强,达到最大程度保持原始结构信息及属性特征的目的。在五个真实属性网络上的实验结果表明,提出的模型在聚类与分类任务中,效果均优于目前流行的表示学习方法。     

自注意力机制的属性异构信息网络嵌入的商品推荐

基于异构信息网络嵌入的推荐技术能够有效地捕捉网络中的结构信息,从而提升推荐性能.然而现有的基于异构信息网络嵌入的推荐技术不仅忽略了节点的属性信息与节点间多种类型的边关系,还忽略了节点不同的属性信息对推荐结果不同的影响.为了解决上述问题,提出一个自注意力机制的属性异构信息网络嵌入的商品推荐(attributed heterogeneous information network embedding with self-attention mechanism for product recommendation, AHNER)框架.该框架利用属性异构信息网络嵌入学习用户与商品统一、低维的嵌入表示,并在学习节点嵌入表示时,考虑到不同属性信息对推荐结果的影响不同和不同边关系反映用户对商品不同程度的偏好,引入自注意力机制挖掘节点属性信息与不同边类型所蕴含的潜在信息并学习属性嵌入表示.与此同时,为了克服传统点积方法作为匹配函数的局限性,该框架还利用深度神经网络学习更有效的匹配函数解决推荐问题.AHNER在3个公开数据集上进行大量的实验评估性能,实验结果表明AHNER的可行性与有效性.