FIELD

The embodiments discussed in the present disclosure are related to seed expansion in a social network using a graph neural network.

BACKGROUND

With advancement in computer technology, there is a rapid increase in information available for people across globe. For example, various experts may share and/or exchange information associated with a particular domain of interest (for example related to science, entertainment, health, art, politics, etc.) through various websites (such as, social media websites or publication websites). Generally, a user or a researcher may require contact information (for example account details) associated with a number of such experts of the particular domain of interest. In certain solutions, the user or researcher may manually search the websites (for example social networks or publication websites) to find such experts to extract relevant information associated with the particular domain of interest. The manual process of search may be time consuming and cumbersome in case a large amount of the contact information available on the social networks. Generally, such domain experts may have a corresponding social media account associated therewith, and the social media accounts may be connected to each other through social networks. Certain solutions may enable to identify such accounts associated with different experts and connected through the social networks. In such solutions, the accounts may be identified based on a structure (such as, graphic representation) of the social networks and various heuristic rules (such as, manual selection based on human judgement). Such solutions may miss the identification of relevant experts which may be hidden (such as, unlabeled social media accounts). Thus, there is a need for an enhanced method to search the social networks in order to identify relevant experts associated with the domain of interest in an effective and reliable manner.

The subject matter claimed in the present disclosure is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one example technology area where some embodiments described in the present disclosure may be practiced.

SUMMARY

According to an aspect of an embodiment, a method may be provided. The method may include retrieving, from one or more websites, a first plurality of seed accounts based on a first user input. The first user input may indicate domain-specific information associated with the first plurality of seed accounts. The method may further include determining a second plurality of seed accounts based on the first plurality of seed accounts associated with the domain-specific information. The second plurality of seed accounts may be determined for training a graph neural network (GNN). The method may further include updating the retrieved first plurality of seed accounts and the determined second plurality of seed accounts based on the trained GNN. The method may further include generating a final set of seed accounts associated with the domain-specific information, based on an application of a predefined ranking criteria on the updated first plurality of seed accounts and the second plurality of seed accounts. The method may further include controlling a display screen to output the generated final set of seed accounts.

According to an aspect of another embodiment, a method may be provided. The method may include extracting, from one or more websites, a first plurality of seed accounts and a second plurality of seed accounts, related to the first plurality of seed accounts, based on domain-specific information. The first plurality of seed accounts and the second plurality of seed accounts may be extracted for training a graph neural network (GNN). The method may further include encoding one or more features of each of the first plurality of seed accounts and each of the second plurality of seed accounts to generate a first vector. The one or more features may correspond to at least one of: a profile description, connection information, or a profile URL. The method may further include selecting a new seed account different from the first plurality of seed accounts and the second plurality of seed accounts. The method may further include extracting, from the one or more websites, a plurality of neighbor seed accounts for the selected new seed account. The method may further include generating a second vector for the selected new seed account based on the extracted plurality of neighbor seed accounts using the trained GNN. The method may further include determining the selected new seed account as a domain-specific seed account or as a non-domain specific seed account based on the generated first vector and the generated second vector. The method may further include updating the first plurality of seed accounts and the second plurality of seed accounts based on the determined new seed account as the domain-specific seed account or as the non-domain specific seed account.

The objects and advantages of the embodiments will be realized and achieved at least by the elements, features, and combinations particularly pointed out in the claims.

Both the foregoing general description and the following detailed description are given as examples and are explanatory and are not restrictive of the invention, as claimed.

