Adaptation of a film is the transfer of a novel to its visual medium. The characters of a story or its adapted film are connected and form two different social networks. The underlying theme of the story may remain unaltered but the relationship among the characters generally deviates in the adapted movie. The difference in the alliance of the characters in a story and its corresponding film is reflected in the extracted networks. The study of these character networks exhibits the deviation as per the demands of the visual medium. We propose an efficient method using different weighted graph centrality metrics such as degree centrality, betweenness centrality, closeness centrality, and edge contribution factor to explore the dissimilarity in the character categorization within a story and its adapted film. We also examine the deviation of the association among the characters according to the time frames using the Mantel test. We analyze two distinguished novels, Nastanirh and Harry Potter and the Philosopher’s Stone , and their celebrated film versions as the case study to indicate how the successful filmmakers interpret the characters in their visual version. The difference in the distribution of the characters in each chapter and the corresponding scene is also analyzed to manifest the convergences and divergences between the two art of expressions. Our approach is distinctive and the analytical results are satisfactory and unbiased than the human perspective.

Analysis of Adapted Films and Stories Based on Social Network

Chinese authors indirectly portray real social relationships in society by incorporating their own experiences and feelings about politics, economic life, and cultural habits. Social and natural knowledge are mixed and hidden in natural literary language. Therefore, by well defining the type of original text, the pipeline for processing the text and the rules to build the graph, we can extract enough valid and useful information. Based on higher-level information, we can build a literary character graph to promote computers’ comprehension of literature. The identification of opposite characters is a challenging topic because the character relationships are intricate and complex. The cryptic expression in literary works can enhance the readability of the plot, but it is obvious that it increases the cost of understanding. Even for human readers different types of characters are difficult to classify and identify, and it usually needs careful reading for several times to get the subtle differences among the characters. Though extracting knowledge from Chinese literature is a complicated and difficult topic, this paper models the graph of polar literary characters and divides polar communities by extracting polar vertices and polar edges based on the concept of literary sentiment polarity. The results show that using a long-sentence window is a good trade-off. The experiments of modern Chinese polar literature show that the accuracy of the community division method for the integrated graph polarity is obviously better than the method based on the co-occurrence network, and it can automatically match the positive and negative communities as well as build the complete graph structure. It clearly reflects the meaning of literary works from the perspective of polarity. This systematic model describes general standard operation steps for the machine to understand complex Chinese literature. The experiments conducted on seven benchmark Chinese novel datasets demonstrate that the method based on emotional polarity shows a significant improvement compared to baseline performance. Though we use Chinese datasets in this paper, the model and methods are important references for literature analysis and graph extraction in other languages.

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Building Character Graphs and Dividing Communities in Chinese Novels Based on Graph Data Extraction: Community Division for Character Emotional Polarity Networks

Social Networks, in the twentieth century, have emerged as the greatest source of mass communication and possibly the best means of information propagation whether it is through Facebook, Twitter, Instagram, WhatsApp, or any other social platform. With the emerging use of ICT and digital globalization, social connections have increased at a rapid rate and are evolving faster with time. This calls for the need for the identification of entities that hold high importance in the network to strategize for information flow in the network. Network centrality or identifying central nodes in the network is a kind of study of this aspect only. There exist nodes of high value based on parameters like high reachability, high accessibility, closeness, etc. which makes the network traffic induced more toward them. Identification of such nodes helps in decision making for propagating information in short steps or in less number of communications in the network, avoiding information traverse from certain paths or curbing information flow, etc. For example, news published in New York Times will certainly be wildfire fast in comparison to when the news is being circulated in social ties of a community only. In this chapter, we come up with an interesting exploration of the centrality measures concept and theory for a network. The chapter focuses on how different centralities play a crucial role in determining the guiding nodes of any action in the network. Further, some existing prevailing works in the literature will be discussed indicating about how the information on the web can help us in identifying the nodes, set of nodes, or networks that are of prime importance based upon which the information flow in the network takes its shape. Experimental simulations over SNAP (Stanford Network Analysis Platform) are conducted to understand the concepts in a more applicative manner. The chapter serves as a basic exploration of the network centrality measures, their applications, and viewpoints to further investigate the measures over more real-world networks to find out more interesting results.

Network Centrality Measures: Role and Importance in Social Networks

The last decades have witnessed the booming of social networks where people interact with each other and generate an unprecedented amount of content. With the online information diffusion, users publish microblog which can affect the sentiments changes of others. The aim of this paper is to analyze the sentimental influence on Sina Microblog users. In this paper, the CNN+ELM model is first proposed, in which the convolution neural network (CNN) and the extreme learning machine (ELM) are combined to identify the sentiment trend of the content. And then, the local sentiment influence rank (LSenInfRank) algorithm and the global sentiment influence rank (GSenInfRank) algorithm based on the sentiment polarity of the content, the user interactive information and the network topology, are proposed. The experimental results on the real Sina Microblog dataset show that the proposed algorithms can effectively find out the set of users with large sentimental influence and have good stability.

An Algorithm of Sina Microblog User’s Sentimental Influence Analysis Based on CNN+ELM Model

Industry is an important pillar of the national economy. Industrial projects are the most complex and difficult projects to control in the construction industry, and major industrial projects are even more complex and difficult to control. Multi-agent coordination is one of the core issues of industrial projects. Based on an analysis of the engineering and construction chains and agent relationships and agent networks of industrial projects, a complex network of the engineering and construction agents of industrial projects is established, and the complex network structural holes theory is applied to study the nonrepeated relationships among agents in industrial projects. Assuming agents are linked through contract relations and the most critical contract index between the agents in the contract amount, through structural hole analysis considering the EPC and PMC model, the aggregate constraint list is obtained, 2D network diagram and 3D network diagram are shown. According to the aggregate constraint value, the EPC contractor with the minimum aggregate constraint value and the project management company with the minimum aggregate constraint value are the critical agent in EPC and PMC model. By analyzing the complex network comprising different models of industrial projects, it is concluded that the characteristics of the agent maintain an advantage in competition, the coordination mechanism of the integration of agent interests, and multi-agent relations are considered to solve the multi-agent coordination problem in major industrial projects.

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Research on the coordination mechanism of major industrial project engineering and construction multi-agents based on structural holes theory.

The analysis of node influence plays important role in product marketing, public opinion analysis, disease transmission and other fields. Researchers have proposed a variety of methods to measure node influence, with the rapid expansion of the scale of social networks, Degree Centrality algorithm attracts much attention for its lowest time complexity, however, its result is not sufficiently accurate because it considers only the local node information and not reflects the impact of topological connections among neighbor nodes. To solve this problem, we proposed a novel measure based on Structural Holes and Degree Centrality(SHDC). Our measure combines Structural Hole and Degree Centrality to measure the node influence. It uses Degree Centrality to make a fast and coarse distinction between the influence of nodes and uses Structure Hole to reflect the impact of topological connections among neighbor nodes, which improves the ability to distinguish the influence of nodes in the low time complexity. Experimental results show that the SHDC algorithm can more accurately measure the influence of nodes than Degree Centrality and Structural Hole and it has stronger applicability.

Xiaohui Zhao

Papers

The Node Influence Analysis in Social Networks Based on Structural Holes and Degree Centrality