SCALA：基于稀疏化的对比学习用于属性网络上的异常检测

Effective and Scalable Clustering on Massive Attributed Graphs

Given a graph G where each node is associated with a set of attributes, and a parameter k specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nodes in G into k disjoint clusters, such that nodes within the same cluster share similar topological and attribute characteristics, while those in different clusters are dissimilar. This problem is challenging on massive graphs, e.g., with millions of nodes and billions of edges. For such graphs, existing solutions either incur prohibitively high costs, or produce clustering results with compromised quality. In this p…

Scalable Anomaly Ranking of Attributed Neighborhoods

Given a graph with node attributes, what neighborhoods are anomalous? To answer this question, one needs a quality score that utilizes both structure and attributes. Popular existing measures either quantify the structure only and ignore the attributes (e.g., conductance), or only consider the connectedness of the nodes inside the neighborhood and ignore the cross-edges at the boundary (e.g., density). In this work we propose normality, a new quality measure for attributed neighborhoods. Normality utilizes structure and attributes together to quantify both internal consistency and external sep…

SINE: Scalable Incomplete Network Embedding

Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content. Because network topology structure and node attributes often exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning good vector representations. In reality, large-scale networks often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms all operate under the assumption that networks are complete. Thus, their…

属性图上具有精度保证的可扩展社区搜索

Scalable Community Search with Accuracy Guarantee on Attributed Graphs

针对属性图社区搜索效率、精度保证及查询相关性问题，提出无索引采样估计方法，支持多种图与社区模型，并在十个真实数据集上显著加速且保证误差。

Change Detection in Dynamic Attributed Networks

A network provides powerful means of representing complex relationships between entities by abstracting entities as vertices, and relationships as edges connecting vertices in a graph. Beyond the presence or absence of relationships, a network may contain additional information that can be attributed to the entities and their relationships. Attaching these additional attribute data to the corresponding vertices and edges yields an attributed graph. Moreover, in the majority of real-world applications, such as online social networks, financial networks and transactional networks, relationships …

EdgeCentric: Anomaly Detection in Edge-Attributed Networks

Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce networks often indicate how users rated products and services in terms of number of stars, and edges in online social and phonecall networks contain temporal information about when friendships were formed and when users communicated with each other -- in such cases, edge attributes capture information about how the adjacent nodes interact with other entities in the network. In this paper, we aim to utilize exactly this …

Multi-attributed Community Search in Road-social Networks

Given a location-based social network, how to find the communities that are highly relevant to query users and have top overall scores in multiple attributes according to user preferences? Typically, in the face of such a problem setting, we can model the network as a multi-attributed road-social network, in which each user is linked with location information and $d$ ($\geq\! 1$) numerical attributes. In practice, user preferences (i.e., weights) are usually inherently uncertain and can only be estimated with bounded accuracy, because a human user is not able to designate exact values with abs…

节点与超边上的有效且高效的属性超图嵌入

Effective and Efficient Attributed Hypergraph Embedding on Nodes and Hyperedges

提出 SAHE 方法，通过引入高阶相似性度量 HMS-N 和 HMS-E 并设计高效优化算法，统一实现属性超图节点与超边嵌入，在嵌入质量和计算效率上显著优于现有方法。

Multi-scale Anomaly Detection on Attributed Networks

Many social and economic systems can be represented as attributed networks encoding the relations between entities who are themselves described by different node attributes. Finding anomalies in these systems is crucial for detecting abuses such as credit card frauds, web spams or network intrusions. Intuitively, anomalous nodes are defined as nodes whose attributes differ starkly from the attributes of a certain set of nodes of reference, called the context of the anomaly. While some methods have proposed to spot anomalies locally, globally or within a community context, the problem remain ch…

Deep Feature Learning for Graphs

This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, DeepGL begins by deriving a set of base features (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher-order. Contrary to previous work, DeepGL learns relational functions (each representing a feature) that generalize across-networks and therefore useful for graph-based …

异构且带属性多层网络中的灵活推断

Flexible inference in heterogeneous and attributed multilayer networks

本文提出一种结合贝叶斯框架、Laplace matching 与自动微分的概率生成模型，以支持任意属性类型的异构多层网络推断，并验证其社区检测、预测和模式发现能力。

十亿规模下的神经属性社区搜索

Neural Attributed Community Search at Billion Scale

本文提出 ALICE 模型，通过密度草图模块度提取候选子图，并利用一致性感知网络 ConNet 建模查询与图的交互，在大规模图上实现高效、准确且可扩展的属性社区搜索。