Query: neo4j

title: Graph communities in Neo4j
abstract: Community discovery is an essential topic in social network analysis since it provides a way for recursively decomposing a large social graph to easily interpretable subgraphs. The implementation of four major community discovery algorithms, namely the Newman Girvan or Edge Betweeness, the Walktrap, the Louvain, and the CNM as Java analytics over Neo4j is described. Their correctness was evaluated functionally in two real Twitter graphs with vastly different characteristics. This was done on the grounds that a successful structural graph partitioning should eventually be reflected in the network functionality domain. Additionally, most real world graphs lack a list of ground truth communities, rendering a structural verification difficult, while functionality can be easily observed in most cases. Naturally, this renders the evaluation network specific, as different social networks have different operational characteristics. The primary algorithmic finding was that the Louvain algorithm yields Twitter communities whose distribution size matches closer, in terms of the Kullback Leibler divergence, the tweet and retweet distributions, with Newman Girvan, Walktrap, and CNM following in that order.

title: Community Detection in Scientific Co Authorship Networks using Neo4j
abstract: Community structure in scientific collaboration network has become an important research area. Co author of a paper can be thought of as a collaborative document between more than one authors. Community detection in co authorship network reveals characteristic patterns of scientific collaboration in computer science research and help to understand the identity organization of the author community. Louvain algorithm is a simple, easy to implement and efficient to recognize community in huge networks. In this paper, it is used to examine the structure of community in Computer University's coauthor network in Myanmar. Neo4j is also used to visualize the co authorship network analysis results. Modularity is used to measure the quality of the cluster structure found by community discovery algorithms. In experiment, Louvain algorithm gives more effective qualitative community structures than other algorithms in co authorship network.

title: The Implementation Knowledge Graph of Air Crash Data based on Neo4j*
abstract: The characteristics of neo4j graph database has been introduced, several methods of constructing graph database based on neo4j importing data have been compared and analyzed, and the "neo4j admin" method has been finally selected to import the air crash data, to realize the visual display and query. Specific implementation steps as follows:(1) Data sets of air crash worldwide since 1908  have been cleaned and preprocessed to remove incomplete lines and unimportant fields;(2) Crashed aircraft information table, accident details table and the above two table association relationship table have been designed and stored in the form of CSV files;(3)The CSV files have been imported into neo4j using the tool "neo4j admin" air crash knowledge graph has been generated; (4) This knowledge graph can be used to query the number of accidents since 1908, the place where the accidents occurred, the number of casualties and the time when the accidents occurred.

title: Can Neo4j Replace PostgreSQL in Healthcare?
abstract: Our current big data landscape prompts us to develop new analytical skills in order to make the best use of the abundance of datasets at hand. Traditionally, SQL databases such as PostgreSQL have been the databases of choice, and newer graph databases such as Neo4j have been relegated to the analysis of social network and transportation datasets. In this paper, we conduct a side by side comparison of PostgreSQL (using SQL) and Neo4j (using Cypher) using the MIMIC III patient database as a case study. We found that, while Neo4j is more time intensive to implement, its queries are less complex and have a faster runtime than comparable queries performed in PostgreSQL. This leads to the conclusion that while PostgreSQL is adequate as a database, Neo4j should be considered a viable contender for health data storage and analysis.

title: Analysis of Physical Access Control System for Understanding Users Behavior and Anomaly Detection Using Neo4j
abstract: Physical Access Control (PAC) systems play a central role in the protection of critical infrastructures, where both the provision of timely access and preserving the security of sensitive areas are paramount. A key challenge is how to detect normal and abnormal behavior of authorized users in PAC systems. This problem becomes increasingly severe as such systems become more and more complex, and is deployed to manage a large amount of sensitive or private areas, buildings and resources. Therefore, the focus of this paper is to develop a Graph based method for analysis of PAC log data to detect normal and abnormal behavior. To achieve this, we have developed an Eclipse application (AC2Neo4j) that transforms PAC log data into Neo4j automatically, thus allowing for powerful analysis to take place using cypher queries.

title: Construction of typhoon disaster knowledge graph based on graph database Neo4j
abstract: The typhoon knowledge graph can correlate various kinds of information in the typhoon data, conduct overall and related analysis, and finally provide effective assistance for typhoon prevention and post disaster protection. The data of typhoon landing in China from 2000 to 2015 were selected to build a typhoon knowledge graph based on Neo4j graph database platform. The typhoon knowledge graph can be used to understand the occurrence of historical typhoons and obtain the distribution of typhoon data in time and space.

title: University Research Graph Database For Efficient Multi Perspective Data Analysis Using Neo4j
abstract: In general, research related data are modeled using a relational database optimized for transaction processing. In many cases, this solution is effective and efficient enough to answer basic queries and simple reporting requirements. However, when users request a more in depth, more expansive, multi perspective, and sometimes more abstract analysis, the relational database struggles to provide answers. This study proposes a research graph database implemented using neo4j as an effort to answer the problems. The database consists of a core model and an extension model. The core model represents scientific articles related data loaded with real data scraped from Google Scholar. The extension model indicates research and community engagement activities done by researchers loaded manually. The database enables the university to analyze researchers' individual and collaborative performances with fellow researchers inside and outside universities. The study concludes that the research graph database implementation is more efficient in answering similar questions than the relational database implementation.

title: Performance Analysis of Neo4j and MySQL Databases using Public Policies Decision Making Data
abstract: Currently, the development of data has increased rapidly, Solutions are needed to be able to manage data efficiently, one that can be offered is to utilize the database. The biggest decision in selecting a database is to select between SQL or NoSQL. MySQL is a database that uses SQL as a query language, consists of tables that store data in the form of columns and rows, then the new format database NoSQL, appeared, it is suitable for handling large amounts of data in a variety of formats. Neo4j is one of NoSQL that is widely used, it is a graph database which provides an easy way to visualize data by storing data in the form of nodes that are connected by edges. In this paper, we compared the performance of MySQL and Neo4j databases in terms of memory usage and execution time, also we presented the flexibility of the databases using P. The results show that MySQL has a faster execution time than Neo4j, although, both these databases have the same time complexity. It is also known that Neo4j has a higher memory usage than MySQL. But Neo4j has better flexibility than MySQL.

title: NBCE: A Neo4j Based Content Extraction Algorithm in Threat Intelligence Web Pages
abstract: Main content extraction is a widely used technique in web crawler, search engines and so on to extract the main content of web pages as well as discarding other complementary and decorative components. By extracting the main content, irrelevant and redundant information can be ignored hence reducing the complexity of data processing and improving the efficiency of further analysis. Among the existing methods tackling this problem, solutions are designed to satisfy the different requirements of various groups. For instance, companies specialized in content extraction always focus more on efficiency and accuracy while others may concentrate more on practicality. In our proposed method, we innovatively present a neo4j based content extraction algorithm (NBCE) in threat intelligence websites. The NBCE algorithm initially transforms the HTML source code into the form of the tree structure. Then the triples extracted from the HTML tree are used to construct a graph based on neo4j database. Finally, by deciding whether a node is the main content node or not, the main content of the given web page can be extracted. The availability of the proposed method is validated through a set of experiments conducted on a threat intelligence related database.

title: Performance Analysis of Neo4j, MongoDB, and PostgreSQL on 2019 National Election Big Data Management Database
abstract: Data has now become a major commodity and asset for any organization. There's also a phenomenon called Big Data where it's a term to describes a large volume of data, both structured and unstructured. With big data, any organization can analyze the data and its results are used for decision making and business strategy. Political parties, for example, use it to analyze the likelihood of their candidate winning in the election based on voter data who votes for their candidate. In Indonesia itself, the use of big data has not been utilized because of limited database infrastructure. Therefore, Indonesia needs a good database that can utilize big data. In this study, the author examines the performance of PostgreSQL, MongoDB, and Neo4J by analyzing each of its complexity using computational complexity theory with Big O notation as its tool. With Big O, the author measures the complexity of each execution time. The conclusion from this study is that MongoDB has excellent performance because it has an O(1) complexity, PostgreSQL has good performance because it has an O(n) and O(1) complexities, and Neo4j has worse performance than MongoDB and PostgreSQL because it has an O(nlog n) complexity.