Events are happening in real-world and real-time, which can be planned and
organized for occasions, such as social gatherings, festival celebrations,
influential meetings or sports activities. Social media platforms generate a
lot of real-time text information regarding public events with different
topics. However, mining social events is challenging because events typically
exhibit heterogeneous texture and metadata are often ambiguous. In this paper,
we first design a novel event-based meta-schema to characterize the semantic
relatedness of social events and then build an event-based heterogeneous
information network (HIN) integrating information from external knowledge base.
Second, we propose a novel Pairwise Popularity Graph Convolutional Network,
named as PP-GCN, based on weighted meta-path instance similarity and textual
semantic representation as inputs, to perform fine-grained social event
categorization and learn the optimal weights of meta-paths in different tasks.
Third, we propose a streaming social event detection and evolution discovery
framework for HINs based on meta-path similarity search, historical information
about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive
experiments on real-world streaming social text data are conducted to compare
various social event detection and evolution discovery algorithms. Experimental
results demonstrate that our proposed framework outperforms other alternative
social event detection and evolution discovery techniques.

本文提出一种基于事件的异构信息网络，结合外部知识库，基于元路径相似度搜索的社交事件检测和演化发现框架，并利用基于加权元路径实例相似性和文本语义表示的新颖 PP-GCN 对社交事件进行细粒度分类，该框架优于其他替代的社交事件检测和演化发现技术。

异构信息网络中的流式社交事件检测和演化发现

Streaming Social Event Detection and Evolution Discovery in  Heterogeneous Information Networks

Analysis and visualization of an information network can be facilitated
better using an appropriate embedding of the network. Network embedding learns
a compact low-dimensional vector representation for each node of the network,
and uses this lower dimensional representation for different network analysis
tasks. Only the structure of the network is considered by a majority of the
current embedding algorithms. However, some content is associated with each
node, in most of the practical applications, which can help to understand the
underlying semantics of the network. It is not straightforward to integrate the
content of each node in the current state-of-the-art network embedding methods.
In this paper, we propose a nonnegative matrix factorization based
optimization framework, namely FSCNMF which considers both the network
structure and the content of the nodes while learning a lower dimensional
representation of each node in the network. Our approach systematically
regularizes structure based on content and vice versa to exploit the
consistency between the structure and content to the best possible extent. We
further extend the basic FSCNMF to an advanced method, namely FSCNMF++ to
capture the higher order proximities in the network. We conduct experiments on
real world information networks for different types of machine learning
applications such as node clustering, visualization, and multi-class
classification. The results show that our method can represent the network
significantly better than the state-of-the-art algorithms and improve the
performance across all the applications that we consider.

本文提出了一种非负矩阵分解 (FSCNMF) 的优化框架，它考虑到网络结构和节点内容的特点，能更好地学习网络的低维向量表示，同时进一步扩展了基本的 FSCNMF 到 FSCNMF++ 来捕捉网络中的高阶亲近性，实验结果表明，与现有算法相比，该方法在节点聚类、可视化和多类别分类等各种机器学习应用中都能显著地提高网络表示的效果。