Social influence estimation is a cornerstone problem for platforms that rely on word-of-mouth dynamics. Identifying influential users underpins targeted advertising, viral marketing campaigns, and behavior prediction models. Prior work has long suggested that similarity between users is a significant driver of influence: people tend to trust, imitate, and adopt behaviors from those who resemble them. Traditional approaches measured that resemblance through structural metrics such as common neighbors, Jaccard coefficients, or path-based distances within the social graph. More recent methods turned to representation learning, embedding users into continuous vector spaces where proximity reflects similarity. Yet, as the authors point out, these techniques capture only limited facets of user characteristics, typically ignoring the rich geographic dimension that location-based social networks uniquely provide.

The study addresses this gap with two progressive contributions. The first, called NSH, short for Node Embedding and Spatial Homophily, is a hybrid, unsupervised method that linearly combines embedding-based structural similarity with spatial homophily, the well-documented tendency of geographically close users to be socially connected and behaviorally alike. NSH is deliberately simple and interpretable: it takes a user similarity score derived from network embeddings and blends it with a spatial homophily score computed from check-in co-location patterns. The result is a transparent similarity measure that serves as a strong baseline for analyzing the relationship between similarity and influence, and one that practitioners can compute without heavy machinery.

The second and central innovation is GeoSocial2Vec, an end-to-end unified embedding model designed to learn a single representation for each user by intrinsically merging social ties and spatial co-visitation patterns. Rather than computing two separate similarity scores and averaging them, GeoSocial2Vec constructs a heterogeneous graph in which users, social links, and locations coexist as nodes of different types. An adaptive random walk mechanism then traverses this combined graph, generating sequences of nodes that alternate between social hops and spatial transitions. These walks are fed into a skip-gram style embedding procedure, so that the resulting vectors encode both who a user knows and where a user goes within one coherent geometric space. The adaptive component weights different transition types dynamically, allowing the model to balance social and spatial evidence according to the structure of the data rather than a fixed heuristic.

This design reflects a broader shift in representation learning toward heterogeneous graph embeddings, where multiple node and edge types are embedded jointly instead of being projected into separate spaces. In the context of location-based social networks, the payoff is substantial. Two users who never interact directly but repeatedly visit the same neighborhoods at similar times will land close together in the GeoSocial2Vec embedding, capturing a latent affinity that pure network metrics would miss. Conversely, two friends who live in different cities and never share locations will still be pulled together by their social tie, something a purely geographic model would overlook. The embedding, in effect, learns the full socio-spatial signature of each user.

To test whether these richer similarity measures actually track influence, the researchers evaluated the relationship between user similarity and social influence through Pearson correlation and regression analysis on geographically filtered segments of three real-world location-based social networks: Gowalla, Brightkite, and Foursquare. These datasets, drawn from check-in services that were popular in the early 2010s, remain standard benchmarks in this field because they pair explicit friendship graphs with millions of timestamped location records. Filtering the data geographically ensured that the spatial component of the analysis was meaningful, focusing on regions dense enough for co-visitation patterns to be statistically robust.

The results were striking. Both proposed methods, in which spatial homophily is integrated with embedding-based similarity, produced a significantly stronger correlation with observed influence patterns than classical approaches that rely on structure alone. The GeoSocial2Vec framework achieved the highest accuracy across the benchmarks, demonstrating that intrinsically coupled socio-spatial learning outperforms post-hoc fusion of separate signals. In other words, it matters not just that social and geographic information are both considered, but that they are woven together during representation learning itself, allowing the model to capture interactions between the two dimensions that a linear combination cannot express.

At the same time, the authors are careful not to declare NSH obsolete. While GeoSocial2Vec offers the best predictive performance, the NSH framework retains clear advantages in interpretability and computational simplicity. Because NSH is a linear fusion of two comprehensible scores, an analyst can inspect exactly how much of a predicted influence relationship stems from network proximity versus geographic overlap. This transparency matters in business applications where stakeholders must justify targeting decisions, and in settings where training a deep heterogeneous embedding model is impractical due to limited compute or data volume. The two frameworks thus occupy complementary niches: GeoSocial2Vec as the accuracy-oriented state of the art, and NSH as a practical, explainable tool for everyday analysis.

The implications extend well beyond academic benchmarking. For advertisers on check-in-driven platforms, influence estimation that accounts for where people go, not just who they know, could sharpen the selection of seed users in viral campaigns, ensuring that promotional content spreads through communities that are both socially and geographically primed to receive it. For behavior prediction, the fused embeddings offer a way to anticipate adoption of products, venues, or even public health behaviors by modeling the twin channels of social contagion and spatial co-presence. Urban planners and mobility researchers may also find the technique useful, since the same embeddings encode movement regularities that correlate with social structure.

The study also contributes to a long-running scientific conversation about homophily, the principle that similar individuals connect and influence one another. Spatial homophily has been documented repeatedly in location-based social networks, where the probability of friendship decays with physical distance and shared places act as meeting grounds for ties. By demonstrating that this geographic dimension measurably strengthens the link between similarity and influence, the research quantifies how much predictive power has been left on the table by purely structural methods. It suggests that future influence models, and by extension the recommendation and marketing systems built on them, should treat location not as metadata but as a first-class signal in the learning process.

Technically, the work sits at the intersection of network embedding, heterogeneous graph learning, and random-walk-based representation methods. The adaptive random walk at the heart of GeoSocial2Vec echoes the logic of metapath-guided walks in heterogeneous graphs, but with a twist: transition probabilities are tuned to the data, letting the model decide how often to follow social edges versus spatial co-visit links. This adaptivity is what allows a single embedding space to serve both dimensions faithfully, and it is likely the key reason the framework outperformed the linear NSH fusion. The evaluation methodology, combining Pearson correlation with regression analysis across three independent platforms, adds confidence that the findings are not an artifact of one dataset’s quirks.

Limitations and open questions remain, as they do in any study of this kind. The benchmark datasets are aging, and modern platforms may exhibit different spatial dynamics, particularly as privacy changes and check-in behavior declines in some services. Influence itself is a latent quantity, inferred from observed diffusion patterns rather than measured directly, so all correlation results inherit the assumptions of the influence estimation pipeline. The authors also note that the published version is an early-access, peer-reviewed accepted manuscript carrying a permanent DOI, subject to final editorial edits. Still, the core message is robust: when it comes to understanding who moves whom in location-based social networks, knowing both the social graph and the map beats knowing either alone.

As location-aware platforms continue to blend commerce, mobility, and social connection, methods like GeoSocial2Vec point toward a future in which influence is predicted not from a single flattened network, but from the rich, coupled reality of how people relate and where they roam. The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors, and the authors declare no competing interests.

Subject of Research: Estimating social influence in location-based social networks using embedding-based similarity and spatial homophily

Article Title: Investigating social influence using embedding-based similarity and spatial homophily on the location based social networks

Article References: Akhavan-Hejazi, Z. S., Ghobaei-Arani, M., & Sajjadi, Z. S. (2026). Investigating social influence using embedding-based similarity and spatial homophily on the location based social networks. Journal of Big Data. https://doi.org/10.1186/s40537-026-01546-x

Image Credits: AI Generated

DOI: 10.1186/s40537-026-01546-x

Keywords: social influence, location-based social networks, node embeddings, spatial homophily, GeoSocial2Vec, heterogeneous graph, random walk, Gowalla, Brightkite, Foursquare, representation learning, targeted advertising