619 research outputs found
Inferring offline hierarchical ties from online social networks
Social networks can represent many different types of relationships between actors, some explicit and some implicit. For example, email communications between users may be represented explicitly in a network, while managerial relationships may not. In this paper we focus on analyzing explicit interactions among actors in order to detect hierarchical social relationships that may be implicit. We start by employing three well-known ranking-based methods, PageRank, Degree Centrality, and Rooted-PageRank (RPR) to infer such implicit relationships from interactions between actors. Then we propose two novel
approaches which take into account the time-dimension of interactions in the process of detecting hierarchical ties. We experiment on two datasets, the Enron email dataset to infer manager-subordinate relationships from email exchanges, and a scientific publication co-authorship dataset to detect PhD advisor-advisee relationships from paper co-authorships. Our experiments show that time-based methods perform considerably better than ranking-based methods. In the Enron dataset, they detect 48% of manager-subordinate ties versus 32% found by Rooted-PageRank. Similarly, in co-author dataset, they detect 62% of advisor-advisee ties compared to only 39% by Rooted-PageRank
Shifu2 : a network representation learning based model for advisor-advisee relationship mining
The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2. © 1989-2012 IEEE
Web of scholars : a scholar knowledge graph
In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science. © 2020 ACM
Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs
Quantitatively profiling a scholar's scientific impact is important to modern
research society. Current practices with bibliometric indicators (e.g.,
h-index), lists, and networks perform well at scholar ranking, but do not
provide structured context for scholar-centric, analytical tasks such as
profile reasoning and understanding. This work presents GeneticFlow (GF), a
suite of novel graph-based scholar profiles that fulfill three essential
requirements: structured-context, scholar-centric, and evolution-rich. We
propose a framework to compute GF over large-scale academic data sources with
millions of scholars. The framework encompasses a new unsupervised
advisor-advisee detection algorithm, a well-engineered citation type classifier
using interpretable features, and a fine-tuned graph neural network (GNN)
model. Evaluations are conducted on the real-world task of scientific award
inference. Experiment outcomes show that the F1 score of best GF profile
significantly outperforms alternative methods of impact indicators and
bibliometric networks in all the 6 computer science fields considered.
Moreover, the core GF profiles, with 63.6%-66.5% nodes and 12.5%-29.9% edges of
the full profile, still significantly outrun existing methods in 5 out of 6
fields studied. Visualization of GF profiling result also reveals human
explainable patterns for high-impact scholars
Detecting hierarchical relationships and roles from online interaction networks
In social networks, analysing the explicit interactions among users can help in
inferring hierarchical relationships and roles that may be implicit. In this thesis,
we focus on two objectives: detecting hierarchical relationships between users and
inferring the hierarchical roles of users interacting via the same online communication
medium. In both cases, we show that considering the temporal dimension of
interaction substantially improves the detection of relationships and roles.
The first focus of this thesis is on the problem of inferring implicit relationships
from interactions between users. Based on promising results obtained by standard
link-analysis methods such as PageRank and Rooted-PageRank (RPR), we introduce
three novel time-based approaches, \Time-F" based on a defined time function,
Filter and Refine (FiRe) which is a hybrid approach based on RPR and Time-F,
and Time-sensitive Rooted-PageRank (T-RPR) which applies RPR in a way that
takes into account the time-dimension of interactions in the process of detecting
hierarchical ties.
We experiment on two datasets, the Enron email dataset to infer managersubordinate
relationships from email exchanges, and a scientific publication coauthorship
dataset to detect PhD advisor-advisee relationships from paper co-authorships.
Our experiments demonstrate that time-based methods perform better in terms of
recall. In particular T-RPR turns out to be superior over most recent competitor
methods as well as all other approaches we propose.
The second focus of this thesis is examining the online communication behaviour
of users working on the same activity in order to identify the different hierarchical
roles played by the users. We propose two approaches. In the first approach, supervised
learning is used to train different classification algorithms. In the second
approach, we address the problem as a sequence classification problem. A novel
sequence classification framework is defined that generates time-dependent features based on frequent patterns at multiple levels of time granularity. Our framework is
a
exible technique for sequence classification to be applied in different domains.
We experiment on an educational dataset collected from an asynchronous communication
tool used by students to accomplish an underlying group project. Our
experimental findings show that the first supervised approach achieves the best mapping
of students to their roles when the individual attributes of the students, information
about the reply relationships among them as well as quantitative time-based
features are considered. Similarly, our multi-granularity pattern-based framework
shows competitive performance in detecting the students' roles. Both approaches
are significantly better than the baselines considered
Learning to Infer Social Ties in Large Networks
Abstract. In online social networks, most relationships are lack of meaning labels (e.g., “colleague ” and “intimate friends”), simply because users do not take the time to label them. An interesting question is: can we automatically infer the type of social relationships in a large network? what are the fundamental factors that imply the type of social relation-ships? In this work, we formalize the problem of social relationship learn-ing into a semi-supervised framework, and propose a Partially-labeled Pairwise Factor Graph Model (PLP-FGM) for learning to infer the type of social ties. We tested the model on three different genres of data sets: Publication, Email and Mobile. Experimental results demonstrate that the proposed PLP-FGM model can accurately infer 92.7 % of advisor-advisee relationships from the coauthor network (Publication), 88.0 % of manager-subordinate relationships from the email network (Email), and 83.1 % of the friendships from the mobile network (Mobile). Finally, we develop a distributed learning algorithm to scale up the model to real large networks.
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