Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification (1612.02814v2)

Abstract: In this paper, we study the problem of author identification under double-blind review setting, which is to identify potential authors given information of an anonymized paper. Different from existing approaches that rely heavily on feature engineering, we propose to use network embedding approach to address the problem, which can automatically represent nodes into lower dimensional feature vectors. However, there are two major limitations in recent studies on network embedding: (1) they are usually general-purpose embedding methods, which are independent of the specific tasks; and (2) most of these approaches can only deal with homogeneous networks, where the heterogeneity of the network is ignored. Hence, challenges faced here are two folds: (1) how to embed the network under the guidance of the author identification task, and (2) how to select the best type of information due to the heterogeneity of the network. To address the challenges, we propose a task-guided and path-augmented heterogeneous network embedding model. In our model, nodes are first embedded as vectors in latent feature space. Embeddings are then shared and jointly trained according to task-specific and network-general objectives. We extend the existing unsupervised network embedding to incorporate meta paths in heterogeneous networks, and select paths according to the specific task. The guidance from author identification task for network embedding is provided both explicitly in joint training and implicitly during meta path selection. Our experiments demonstrate that by using path-augmented network embedding with task guidance, our model can obtain significantly better accuracy at identifying the true authors comparing to existing methods.

• The paper proposes a task-guided, path-augmented network embedding model that addresses author identification challenges in anonymized review settings.
• The model innovatively integrates meta path selection with joint training, achieving notable gains in Mean Average Precision and Recall compared to baseline methods.
• Its versatile design lays the groundwork for extending the framework to other heterogeneous network mining tasks, enhancing automated feature representation.

Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification

The paper "Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification" introduces an innovative model addressing the problem of author identification in double-blind review settings. The research leverages heterogeneous network embedding to bypass the reliance on conventional feature engineering methods, thereby facilitating a more automatic and robust representation of nodes in lower-dimensional feature spaces. The paper identifies two principal challenges: the need for task-specific network embeddings and the necessity to adequately leverage the heterogeneous nature of networks for information selection.

The proposed solution is a task-guided and path-augmented network embedding model tailored to enhance author identification accuracy in anonymized peer-reviewed contexts. This model operates by embedding nodes as vectors within a latent feature space, supporting both task-specific and network-general objectives. Notably, the research innovates by incorporating meta paths to account for network heterogeneity and guides the network embedding process through task-specific learning objectives.

Key strong points of the model are substantiated through its significant improvement over existing methods in accurately identifying true authors, as evidenced by the numerical results. The model’s design, which integrates task guidance explicitly through joint training and implicitly via meta path selection, marks a methodical advancement from conventional network embedding techniques that typically ignore heterogeneity and lack task sensitivity.

Experimentation with the model demonstrated superior author identification performance, achieving considerable gains in Mean Average Precision and Recall metrics compared to baseline models. This suggests that the model's combined approach effectively navigates the challenges of heterogeneous network structures and tailored task objectives.

The implications of this work are substantial, offering a method that is adaptable to various network mining problems while remaining focused on specific tasks like author identification. The paper also speculates that the proposed framework could be extended to resolve other task-oriented embedding issues within heterogeneous networks, thereby presenting new avenues for future research in AI and network analysis.

Moreover, the meta-path selection mechanism functions as a significant augmentation to the model's efficacy, guiding consistent improvements in embedding quality tailored to specific network tasks. The efficiency of the model, ensured by its parallelizable learning algorithm, enables processing large-scale, complex networks typical of bibliographic datasets.

Ultimately, this research contributes to advancing computational approaches in network mining, emphasizing the synthesis of task-driven methodologies with sophisticated network embeddings. This alignment potentially leads to more perceptive and contextually aware AI systems capable of deciphering intricate patterns within heterogeneous data landscapes. Future developments may focus on extending the model’s applicability and refining its capacity for text analysis and comprehensive author set prediction.