Analyzing future communities in growing citation networks

Jung, Sukhwan; Segev, Aviv

doi:10.1016/j.knosys.2014.04.036

Cited by 23 publications

(9 citation statements)

References 35 publications

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“…New keywords are hard to simulate since the content of the possible label of keywords is harder to extract. However, previous work suggested that analyzing papers by clusters of topics can assist in identifying possible labels of new clusters [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Invisible Brain: Knowledge in Research Works and Neuron Activity

et al. 2016

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If the market has an invisible hand, does knowledge creation and representation have an “invisible brain”? While knowledge is viewed as a product of neuron activity in the brain, can we identify knowledge that is outside the brain but reflects the activity of neurons in the brain? This work suggests that the patterns of neuron activity in the brain can be seen in the representation of knowledge-related activity. Here we show that the neuron activity mechanism seems to represent much of the knowledge learned in the past decades based on published articles, in what can be viewed as an “invisible brain” or collective hidden neural networks. Similar results appear when analyzing knowledge activity in patents. Our work also tries to characterize knowledge increase as neuron network activity growth. The results propose that knowledge-related activity can be seen outside of the neuron activity mechanism. Consequently, knowledge might exist as an independent mechanism.

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Section: Discussionmentioning

confidence: 99%

Invisible Brain: Knowledge in Research Works and Neuron Activity

et al. 2016

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“…A network-based approach was proposed to overcome the rigidity of trend-based forecasting where the prediction is dependent on the type and shape of the technology growth curve used. Node prediction based on preferential attachment link prediction is proposed to classify whether the nodes in citation networks have a connection to a new node in the future [21], labeling the new nodes by utilizing the metadata of their neighboring nodes [22]. This showed that predicting nodes in bibliographic networks is possible based on the structural properties of the network.…”

Section: Identifying and Predicting New Topicsmentioning

confidence: 99%

Analyzing the generalizability of the network-based topic emergence identification method

Jung

Segev

2022

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Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.

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“…Jung and Segev [41] proposed methods to analyze how communities change over time in the citation network graph without additional external information and based on node and link prediction and community detection.…”

Section: Mappingmentioning

confidence: 99%

“…For the agglomerative methods of CD, there are two commonly used algorithms: first, Newman's CD algorithm is a widely used agglomerative method that uses modularity to measure the goodness of the current partitioning; second, the recently developed Louvain method [65] is an agglomerative method and is commonly used because of its low computational complexity and high performance. When merging communities, the Louvain method considers not only the modularity but also the consolidation ratio [41]. Newman's algorithm is effective but slow, whereas Louvain's method is much more computationally efficient [66].…”

Section: Measure the Complex Network Structural Features Of The Sprtsmentioning

confidence: 99%

SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques

Suh

2019

Sustainability

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In the digital age, the abundant unstructured data on the Internet, particularly online news articles, provide opportunities for identifying social problems and understanding social systems for sustainability. However, the previous works have not paid attention to the social-problem-specific perspectives of such big data, and it is currently unclear how information technologies can use the big data to identify and manage the ongoing social problems. In this context, this paper introduces and focuses on social-problem-specific key noun terms, namely SocialTERMs, which can be used not only to search the Internet for social-problem-related data, but also to monitor the ongoing and future events of social problems. Moreover, to alleviate time-consuming human efforts in identifying the SocialTERMs, this paper designs and examines the SocialTERM-Extractor, which is an automatic approach for identifying the key noun terms of social-problem-related topics, namely SPRTs, in a large number of online news articles and predicting the SocialTERMs among the identified key noun terms. This paper has its novelty as the first trial to identify and predict the SocialTERMs from a large number of online news articles, and it contributes to literature by proposing three types of text-mining-based features, namely temporal weight, sentiment, and complex network structural features, and by comparing the performances of such features with various machine learning techniques including deep learning. Particularly, when applied to a large number of online news articles that had been published in South Korea over a 12-month period and mostly written in Korean, the experimental results showed that Boosting Decision Tree gave the best performances with the full feature sets. They showed that the SocialTERMs can be predicted with high performances by the proposed SocialTERM-Extractor. Eventually, this paper can be beneficial for individuals or organizations who want to explore and use social-problem-related data in a systematical manner for understanding and managing social problems even though they are unfamiliar with ongoing social problems.

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Analyzing future communities in growing citation networks

Cited by 23 publications

References 35 publications

Invisible Brain: Knowledge in Research Works and Neuron Activity

Invisible Brain: Knowledge in Research Works and Neuron Activity

Analyzing the generalizability of the network-based topic emergence identification method

SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques

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