Academic Influence Aware and Multidimensional Network Analysis for Research Collaboration Navigation Based on Scholarly Big Data

Abstract

Scholarly big data, which is a large scale collection of academic information, technical data, and collaboration relationships, has attracted increasing attentions, ranging from industries to academic societies. The widespread adoption of social computing paradigm has made it easier for researchers to join collaborative research activities, and share the academic data more extensively than ever before across the highly interlaced academic networks. In this study, we focus on the academic influence aware and multidimensional network analysis based on the integration of multi-source scholarly big data. Following three basic relations: Researcher-Researcher, Researcher-Article, and Article-Article, a set of measures is introduced and defined to quantify correlations in terms of activity-based collaboration relationship, specialty-aware connection, and topic-aware citation fitness among a series of academic entities (e.g., researchers and articles) within a constructed multidimensional network model. An improved Random Walk with Restart (RWR) based algorithm is developed, in which the time-varying academic influence is newly defined and measured in a certain social context, to provide researchers with research collaboration navigation for their future works. Experiments and evaluations are conducted to demonstrate the practicability and usefulness of our proposed method in scholarly big data analysisusing DBLP and ResearchGate data.

Improved LSH for privacy-aware and robust recommender system with sparse data in edge environment

“…For simple experiments, we only test the discrete data in Movielens dataset without testing other data types popular in the big data environment, e.g., continuous data type [25][26][27][28][29], Boolean data type [30], and fuzzy data type [31][32][33]. In addition, we only test a single criterion (user rating) without discussing the popular cases with multiple criteria [34][35][36][37][38][39][40][41][42][43][44] and their inner linear correlations [45][46][47][48], nonlinear correlations [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66], and weights [67][68][69][70][71][72]…”

Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

“…Figure 2 shows that a user needs to perform the following keywords research tasks [15] before his creation: (1) paper recommendation (i.e., k 1 ) for paper recommendation process research [16]; (2) keyword search (i.e., k 2 ) for keyword search research and applying it to paper recommendation process; (3) Steiner tree (i.e., k 3 ) for Steiner algorithm [17] research and applying it to keyword search; (4) dynamic programming (i.e., k 6 ) for dynamic programming technique research and applying it to solve Steiner tree problem. In Figure 2, the user obtains four corresponding keywords (i.e., Q � {k 1 , k 2 , k 3 , k 6 }) by the preliminary analysis of his research content [18]. Next, the user can search some corresponding papers from Figure 3.…”

“…End If (14) Break (15) End If (16) Else tree growth (17) Else tree merging (18) Return Q 1 (19) Return Complexity from 2 to 6. Furthermore, the quantity of recommended papers in our approach increases with the number of query keywords increasing, which is because the returned solutions including more papers can satisfy more query keywords requirements of users.…”

Temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation

“…Generally speaking, collaborative filtering recommendation methods can be grouped into two common classes, ie, memory-based CF algorithms and model-based CF algorithms. [25][26][27] Memory-based CF algorithms, also known as neighborhood-based CF model, which makes predictions based on the history rating data and mines the similarity relationship among items or users. Meng et al 26 an integrated CF-based recommendation method that integrates item ratings, user ratings, and social trust to improve the recommendation performance.…”