Topic modeling with network regularization

Mei, Qiaozhu; Cai, Deng; Zhang, Duo; Zhai, ChengXiang

doi:10.1145/1367497.1367512

Cited by 320 publications

(188 citation statements)

References 26 publications

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“…A natural approach to network-based topic modeling is to add a network-based regularization constraint to traditional topic models such as NetPLSA [65]. The relational topic model (RTM) proposed in [23] tries to model the generation of documents and links sequentially.…”

Section: Clustering Text In Networkmentioning

confidence: 99%

A Survey of Text Clustering Algorithms

2012

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Clustering is a widely studied data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this chapter, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the text domain. We will discuss the key methods used for text clustering, and their relative advantages. We will also discuss a number of recent advances in the area in the context of social network and linked data.

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Section: Clustering Text In Networkmentioning

confidence: 99%

A Survey of Text Clustering Algorithms

2012

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“…For example, the Author-Conference-Topic model (ACT) [14] treats authors and venues as probability distributions over topics extracted by means of an unsupervised learning technique. Mei et al [15] propose a framework to model topics by regularizing a statistical topic model through a harmonic regularizer, which is based on a graph structure. Differently from these methods, we exploit an automatically generated knowledge base [3] to characterize research topics semantically and we use this as the basis for associating a diachronic semantic topic distribution with each author.…”

Section: State Of the Artmentioning

confidence: 99%

Identifying Diachronic Topic-Based Research Communities by Clustering Shared Research Trajectories

Osborne

Scavo

Motta

2014

Lecture Notes in Computer Science

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Abstract. Communities of academic authors are usually identified by means of standard community detection algorithms, which exploit 'static' relations, such as co-authorship or citation networks. In contrast with these approaches, here we focus on diachronic topic-based communities -i.e., communities of people who appear to work on semantically related topics at the same time. These communities are interesting because their analysis allows us to make sense of the dynamics of the research world -e.g., migration of researchers from one topic to another, new communities being spawn by older ones, communities splitting, merging, ceasing to exist, etc. To this purpose, we are interested in developing clustering methods that are able to handle correctly the dynamic aspects of topic-based community formation, prioritizing the relationship between researchers who appear to follow the same research trajectories. We thus present a novel approach called Temporal Semantic Topic-Based Clustering (TST), which exploits a novel metric for clustering researchers according to their research trajectories, defined as distributions of semantic topics over time. The approach has been evaluated through an empirical study involving 25 experts from the Semantic Web and Human-Computer Interaction areas. The evaluation shows that TST exhibits a performance comparable to the one achieved by human experts.

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“…For the more challenging Cora dataset, Table 3 shows the results on Cora dataset. We compare the performance of RankCompete and with the state-of-the-arts algorithms including Normalized Cut(NC) [7], NetPLSA [11], RankClus [8] and iTopicModel [10]. The results show that our algorithm outperforms the state of the art algorithms in clustering information networks.…”

Section: Bibliography Networkmentioning

confidence: 99%

RankCompete: Simultaneous ranking and clustering of information networks

Cao¹,

Jin

Yin

et al. 2012

Neurocomputing

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Random walk was first introduced by Karl Pearson in 1905 and has inspired many research works in different fields. In recent years, random walk has been adopted in information network research, for example, ranking and similarity estimation. In this paper, we introduce a new model called RankCompte, which allows multiple random walkers in the same network (existing work mostly focus on random walks of a single category). By introducing the "competition" concept into the random walk framework, our method can fulfill both clustering and ranking tasks simultaneously and thus provide an effective analysis tool for networks. Compared with the traditional network ranking approaches, our new method focuses more on the structure of specialized clusters. Compared with the traditional graph clustering approaches, our new method provides a faster and more intuitive way to group the network nodes. We validate our approach on both bibliography networks and visual information networks, and the results show that our approach can obtain 100% perfect clustering results in clustering the DBLP 20 conferences and outperform the state-of-the-art on the Cora dataset. Furthermore, we show that our method can be effectively used to summarize personal photo collections.

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Topic modeling with network regularization

Cited by 320 publications

References 26 publications

A Survey of Text Clustering Algorithms

A Survey of Text Clustering Algorithms

Identifying Diachronic Topic-Based Research Communities by Clustering Shared Research Trajectories

RankCompete: Simultaneous ranking and clustering of information networks

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