Learning Heterogeneous Subgraph Representations for Team Discovery (original) (raw)

A Neural Approach to Forming Coherent Teams in Collaboration Networks

2022

We study team formation whose goal is to form a team of experts who collectively cover a set of desirable skills. This problem has mainly been addressed either through graph search techniques, which look for subgraphs that satisfy a set of skill requirements, or through neural architectures that learn a mapping from the skill space to the expert space. An exact graph-based solution to this problem is intractable and its heuristic variants are only able to identify sub-optimal solutions. On the other hand, neural architecture-based solutions treat experts individually without concern for team dynamics. In this paper, we address the task of forming coherent teams and propose a neural approach that maximizes the likelihood of successful collaboration among team members while maximizing the coverage of the required skills by the team. Our extensive experiments show that the proposed approach outperforms the state-of-the-art methods in terms of both ranking and quality metrics.

Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction

ACM Transactions on Knowledge Discovery from Data

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborato...

An Overview on Network Representation Learning

2021

Representation learning has proven its usefulness in many activities such as photography and text mining. The goal of network representation learning is to learn distributed vector representation for each vertex in the networks, an essential feature of network analysis is now increasingly recognised. Some techniques of network representation research network systems for learning. In effect, vertices of the network contain rich data (such as text), that cannot be used with the traditional algorithmic frameworks. We suggest DeepWalk in text-associated form, by showing that DeepWalk, a high-tech network representation solution, is equal to matrix factorisation (TADW). In the context of matrix factorisation, TADW introduce text features of vertices in network representation research. Through applying them to the multi classifying of vertices, we compare our system and different baseline methods.The experimental results show that, our method outperforms other baselines on all three datas...