Evaluating Link Prediction Accuracy on Real-World Networks Under Different Missing Edge Patterns

Temel Kavramlar

The accuracy of link prediction algorithms varies significantly depending on the underlying missing edge pattern in real-world network datasets.

Özet

This study investigates the impact of different missing edge patterns on the performance of link prediction algorithms across a diverse set of 250 real-world network datasets from 6 different domains. The key findings are:

The Top-Stacking ensemble learning method consistently outperforms other link prediction algorithms across most domains and missing edge patterns. However, more specialized methods like Node2Vec Edge Embedding and Preferential Attachment can perform better in certain domains and under specific missing edge patterns.

The depth-first search (DFS) based missing edge pattern leads to lower link prediction accuracy compared to other missing edge patterns across most domains and algorithms. This suggests that DFS samples may have inherent structural features that make them more challenging for link prediction.

The performance of link prediction algorithms varies significantly across different network domains. For example, in social networks, the accuracy is highly dependent on the missing edge pattern, with node-based patterns performing better than edge-based patterns.

Knowing the domain of the network and the underlying missing edge pattern can help researchers select the most appropriate link prediction algorithm for their data, rather than relying on a one-size-fits-all approach.

İstatistikler

"Real-world network datasets are typically obtained in ways that fail to capture all edges. The patterns of missing data are often non-uniform as they reflect biases and other shortcomings of different data collection methods."
"Missing data in social network analysis can result from survey non-response among a closely connected group of individuals, rather than being uniformly distributed."
"Missing friendship relationships often occur among outliers within a group who are not closely connected to the rest of the group, unlike other nodes."
"Missing data in protein-protein interaction networks can lead to missing edges related to all of the missing proteins, providing no information about the relevant nodes."

Alıntılar

"Assuming uniform random missing-edge patterns can thus be inappropriate for link prediction in real-world networks."
"Our findings emphasize the importance of taking into account the dataset domain and associated missing-edge pattern, particularly how the data was sampled, when choosing an appropriate prediction algorithm in a specific setting."

Önemli Bilgiler Şuradan Elde Edildi

Link Prediction Accuracy on Real-World Networks Under Non-Uniform Missing Edge Patterns

by Xie He,Amir ... : arxiv.org 05-02-2024

https://arxiv.org/pdf/2401.15140.pdf

Daha Derin Sorular

How can the insights from this study be extended to link prediction in temporal or multilayer networks, where the missing edge patterns may vary across different time steps or network layers

The insights from this study can be extended to link prediction in temporal or multilayer networks by considering the dynamic nature of missing edge patterns across different time steps or network layers. In temporal networks, where edges may appear and disappear over time, understanding the temporal evolution of missing edge patterns is crucial for accurate link prediction. By incorporating the concept of time into the analysis, researchers can track how missing edge patterns change over time and adapt link prediction algorithms accordingly. This could involve developing algorithms that can capture the temporal dependencies in missing edge patterns and adjust link prediction strategies based on these dynamics.
Similarly, in multilayer networks, where different types of interactions or relationships exist across multiple layers, the missing edge patterns may vary across different network layers. By analyzing missing edge patterns in the context of multilayer networks, researchers can explore how different types of interactions influence link prediction performance. This could involve developing specialized link prediction algorithms that can effectively handle the complexity of multilayer networks and adapt to the unique missing edge patterns present in each layer.
Overall, extending the insights from this study to temporal or multilayer networks involves considering the temporal or multilayer nature of the networks and how missing edge patterns evolve over time or across different layers to improve link prediction accuracy.

What network features, beyond just the domain, could be used to better characterize the relationship between missing edge patterns and link prediction performance

Beyond just the domain, several network features could be used to better characterize the relationship between missing edge patterns and link prediction performance. Some key network features that could be considered include:

Community Structure: The presence of communities or clusters in a network can impact missing edge patterns and link prediction accuracy. Analyzing community structure can provide insights into how missing edges are distributed within and between communities, influencing link prediction outcomes.

Centrality Measures: Node centrality measures, such as degree centrality, betweenness centrality, and eigenvector centrality, can reveal important nodes in the network that may be more prone to missing edges. Understanding the centrality of nodes can help in identifying critical missing edge patterns for link prediction.

Network Density: The overall density of the network, as well as local density around specific nodes or edges, can affect missing edge patterns and link prediction performance. Sparse or dense regions in the network may exhibit different missing edge patterns, impacting the effectiveness of link prediction algorithms.

Temporal Dynamics: In temporal networks, considering the temporal dynamics of edge formation and disappearance can provide valuable insights into missing edge patterns. Analyzing the timing of missing edges and their impact on link prediction over time can enhance the accuracy of temporal link prediction algorithms.

By incorporating these network features into the analysis, researchers can gain a more comprehensive understanding of how missing edge patterns influence link prediction performance and tailor their approaches accordingly.

Can the principles identified in this study be applied to other network analysis tasks beyond link prediction, such as community detection or node classification

The principles identified in this study can be applied to other network analysis tasks beyond link prediction, such as community detection or node classification, by considering the impact of missing edge patterns on these tasks.

Community Detection: Understanding missing edge patterns can help in identifying hidden or spurious connections within communities, which can affect the accuracy of community detection algorithms. By accounting for missing edges and their patterns, researchers can refine community detection methods to better capture the underlying structure of the network.

Node Classification: Missing edge patterns can introduce biases or uncertainties in node classification tasks, where the presence or absence of edges between nodes influences the classification outcome. By analyzing missing edge patterns, researchers can develop robust node classification algorithms that account for the variability in edge information and improve the accuracy of node labeling.

Network Reconstruction: In scenarios where the network structure is incomplete or noisy, understanding missing edge patterns is essential for network reconstruction tasks. By leveraging insights from link prediction studies on missing edge patterns, researchers can enhance network reconstruction algorithms to fill in missing information and reconstruct the network more accurately.

By applying the principles of considering missing edge patterns to these network analysis tasks, researchers can improve the robustness and accuracy of their algorithms across various applications in network science.