How do you incorporate domain knowledge and user feedback in network alignment?

1 Use domain-specific similarity measures

One of the key challenges of network alignment is to define a suitable similarity measure that captures the similarity or relatedness of nodes or edges across networks. A generic similarity measure, such as cosine similarity or Jaccard similarity, may not reflect the domain-specific characteristics or constraints of the networks. For example, in social networks, you may want to consider not only the profile attributes or the network structure of the nodes, but also their interactions, preferences, or behaviors. Therefore, you should use domain-specific similarity measures that incorporate domain knowledge and capture the relevant aspects of the networks. For example, you can use topic models, sentiment analysis, or collaborative filtering to measure the similarity of nodes based on their textual, emotional, or rating data.

2 Use feedback-driven alignment

Another way to incorporate domain knowledge and user feedback in network alignment is to use feedback-driven alignment, which is an interactive and iterative process that allows users to provide feedback on the alignment results and refine them accordingly. Feedback-driven alignment can be useful for dealing with uncertainty, ambiguity, or incompleteness of the network data, as well as for adapting to the user's preferences or goals. For example, you can use active learning, reinforcement learning, or preference elicitation techniques to ask users to confirm, reject, or rank the alignment candidates, and then use their feedback to update the similarity measure, the alignment algorithm, or the alignment objective.

3 Use network embedding

Another technique that can help you incorporate domain knowledge and user feedback in network alignment is network embedding, which is the process of representing the nodes or edges of a network as low-dimensional vectors that preserve their structural or semantic properties. Network embedding can be useful for reducing the complexity and dimensionality of the network data, as well as for facilitating the comparison and alignment of different networks. For example, you can use deep learning, graph neural networks, or matrix factorization techniques to learn the network embeddings, and then use them as inputs for the alignment algorithm. Moreover, you can use domain knowledge or user feedback to guide or constrain the network embedding process, such as by incorporating prior knowledge, labels, or preferences into the embedding objective or loss function.

4 Use network alignment evaluation

Finally, you should use network alignment evaluation to assess the quality and relevance of the alignment results, as well as to identify the strengths and weaknesses of your alignment approach. Network alignment evaluation can be done using various metrics and methods, depending on the type and purpose of the alignment. For example, you can use accuracy, precision, recall, or F1-score to measure the correctness of the alignment, or use NDCG, MAP, or MRR to measure the ranking quality of the alignment. Moreover, you can use domain knowledge or user feedback to evaluate the alignment results from a user-centric perspective, such as by using user satisfaction, utility, or trust as evaluation criteria.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?