Short text classification is an important natural language processing task due to the prevalence of short text on the internet and social media platforms.In this paper, we propose a novel graph-based short text classification method named GBBM (Graph-BERT-BTM Model) that leverages the powerful representation ability of graph data to capture the structural features of short text.In this work, we incorporate topic information to enrich and expand the feature space for the short text and compare our proposed method on five publicly available short text datasets with five existing models.Experimental results indicate the superiority of our proposed method.

Identifying influential spreaders is a hot topic in complex network research.While centrality-based algorithms are easy to implement, they often have lower accuracy.Topologybased algorithms are effective for identifying network center influential spreaders but may not perform well in identifying peripheral influential spreaders.To address these problems, we propose a Neighborhood Structure Centrality (NSC) algorithm, which utilizes structural embedding and clustering to collect various network structural information and calculates node influence based on both node and neighborhood structural information.We compare the NSC algorithm with twelve baseline algorithms on six public datasets and four synthetic network datasets and demonstrate its higher accuracy.

With the development of network science, complex network analysis has received extensive attention. In recent years, influential spreaders identification has become a hot topic in the research of complex networks. In general, influential spreader identification algorithms are mainly divided into centrality-based algorithms and topology-based algorithms. However, centrality-based algorithms have to face the information limitation problem that leads to low accuracy for identifying influential spreaders. Topology-based algorithms have both structural and positional limitation problems that lead to low accuracy for identifying peripheral influential spreaders. In this paper, we focus on improving the situation and propose two influential spreader identification algorithms, NSC (neighborhood structure centrality) and NPC (neighborhood position centrality) from both the perspective of the centrality and the network topology. NSC algorithm collects various types of network structure information through structure embedding and clustering, so as to solve missing network structure information problem. NPC algorithm calculates neighborhood location information by improving the k-shell algorithm to tackle location limitation problem. Experimental results with fourteen baseline algorithms show that our proposed algorithms NSC and NPC can achieve higher accuracy.

As for standard topic model, such as LDA (Latent Dirichlet Allocation), each topic is generally depicted by a weighted word set, where the high-ranked words are deemed more representative.Meanwhile, the probability of each word is considered as the ability to represent the semantic contribution for the topic.However, few efforts are focused on enhancing the representative ability of the topic to support fine grained topic representation.In this paper, we propose a Word Topic Ware (WTW) model to take word inherent diversity characteristic into consideration, in order to screen out and enhance the more representative words for topic representation.Experimental results on three large datasets show that our proposed method can increase the representative ability for topic representation.In addition, our work will positively affect improving the quality of topic content analysis.