Purpose

In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes.

Design/methodology/approach

The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features.

Findings

The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines.

Originality/value

Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

中文翻译：

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基于社交网络的方法，用于识别和排序智慧城市的影响节点

目的

在在线社交网络分析中，基于影响节点的突出性来识别和排序问题已引起研究人员和从业者的极大关注。使用Twitter识别和排序有影响力的节点是一个具有挑战性的问题，因为数据包含异类功能，例如推文，喜欢，提及和转推。本文的目的是在各种特征，评估指标，方法和结果之间进行关联，以验证特征和结果的选择。此外，本文还使用了众所周知的技术来查找影响节点的主题权限和情感，以帮助智慧城市治理并做出重要决策，同时了解相关影响节点的各种看法。

设计/方法/方法

使用Twitter API提取的推文存储在Neo4j中，以在Twitter数据的各种功能（例如关注者，提及和转发）之间生成基于图的关系。在本文中，已经提出了一种基于Twitter数据的共识方法，该方法使用各种特征（例如，提及和转发）基于异构数据使用Twitter数据，以基于每个特征生成前k个有影响力的节点的单独列表。

发现

异构功能旨在用于以较低的计算复杂度（即O（n））集成以完成识别和排名任务，适用于大规模在线社交网络，其准确性要高于基线。

创意/价值

确定的有影响力的节点可以作为做出公共决策的来源，他们的意见可以为城市治理机构（例如市政公司以及负责智慧城市治理和智慧城市发展的类似组织）提供见解。