Location-Aware Social Network Recommendation via Temporal
Graph Networks

Ziyi Zhang, Diya Li, Zhenlei Song, Nick Duffield, and Zhe Zhang
Texas A&M University, College Station, TX

November 13, 2023

Zhang et al.

Texas A&M University

1 / 11

Introduction

1

In the data-driven era, recommendations have become indispensable acrossvarious systems.

2

Link prediction, a cornerstone of recommendations, excels in forecasting
future network connections based on current structures.

3

Today’s dynamic networks continually reshape connections, introducing newlinks and nodes while removing others.

4The inclusion of location information associated with nodes provides a newopportunity.

5Adapting models to this dynamism necessitates capturing spatial andtemporal dependencies for sustained effectiveness.

Zhang et al.

Texas A&M University

2 / 11

Problem Formulation

Define a dynamic graph as Gt = (V, Et ), which evolves over discrete timeintervals. In this representation, V represents nodes, potentially corresponding toentities like users or researchers. Importantly, V can exhibit dynamic
characteristics, including attributes such as location and interests. Meanwhile, Etsignifies the temporal connections between nodes at time t, with each edge
bearing a timestamp. By selectively removing designated edges, denoted as
Ei ∈ Et , from Gt , we derive a new connected graph, denominated as
Gt−1 = (V, Et − Ei ), positioned at time t − 1. The primary objective of link
prediction-based recommendation is to forecast the probability of Ei resurfacing in
Gt given the structural information of Gt−1 .

Zhang et al.

Texas A&M University

3 / 11

Methodology

1Location-Aware Node-Wise Event: Represented by loci , where i signifies
the node index, and the 2-D vector loc represents a location with latitude
and longitude coordinates. If node i has not been encountered previously, thisevent creates it with its current location; otherwise, it updates the location
information.

2

Interaction Event: Represented by an edge eij (t) connecting nodes i and j.
In this context, eij (t) is treated as a vector that characterizes the interaction
between nodes i and j. For instance, it can capture shared research interests
between node i and node j in a collaboration social network.
Zhang et al.

Texas A&M University

4 / 11

1

An interaction event that involves nodes i and j. In this context, we define
two essentials update functions as follows:yi (t) = fij (pi (t− ), pj (t− ), ∆t, eij (t))
yj (t) = fji (pj (t− ), pi (t− ), ∆t, eij (t))

2

A location-aware node-wise event loci , we update it using:yi (t) = fi (pi (t− ), t, loci (t))

3

An information aggregator can be employed:y i (t) = Aggregator(yi (t1 ), ..., yi (tb ))4Updating pi (t) using a Long Short-Term Memory mechanism:pi (t) = LSTM(y i (t), pi (t− ))5Produce the embedding zi (t) for node i at time t:
Xzi (t) =h(pi (t), pj (t), eij , loci (t), locj (t))[0,t]j∈NiZhang et al.

Texas A&M University

5 / 11

Dataset
Description of Collected Features in Scholars@TAMU Dataset
Name‘author id’‘publication id’‘publication year’‘abstract’‘location’

Description
An author id is a distinct and anonymous identifier assigned to each faculty member within the dataset.
A publication id is a unique and anonymous identifier assigned to each publication within the dataset.
The publication year denotes the specific year in which an article was published or made publicly accessible.
The abstract, linked to the publication id, provides a concise summary of each distinct publication within the dataset.
The location attribute contains information about each author’s educational institution and current workplace.

Scholars@TAMU is a valuable platform that helps faculty members andorganizations at Texas A&M University (TAMU) showcase their expertise. Itgathers information from various sources, including TAMU’s systems, publicresearch data (like grants and publications), and authoritative references. Thisdata is then used to create individual profiles that faculty members can edit toaccurately represent their skills and knowledge. For our research, we collected 13k
samples from Scholars@TAMU over several decades.

Zhang et al.

Texas A&M University

6 / 11Data Pre-processing

In our data preprocessing pipeline, we carefully process the features to ensure theirsuitability for subsequent analysis. Specifically, the ‘author id’ and ‘publication id’samples are subjected to expansion and uniqueness operations. Meanwhile, the‘publication year’ samples are normalized by scaling them to fall within the range
defined by their maximum and minimum values. In the case of ‘abstract’ features,
we employ doc2vec for vectorization, thereby capturing the semantic essence oftext. The ‘location’ features are uniquely managed by representing them as
2-dimensional vectors that incorporate longitude and latitude information.
Zhang et al.

Texas A&M University

7 / 11

Evaluation Metrics

1AUC-ROC (Area Under the Curve - Receiver Operating Characteristic) andAP (Average Precision) are used to evaluate our link prediction model.

2AUC-ROC measures overall classification performance, capturing the area
under the ROC curve which plots True Positive Rate against False PositiveRate. Higher scores indicate better discrimination ability.

3

AP summarizes precision-recall performance by calculating the weighted
mean of precision achieved at each threshold, emphasizing precision atdifferent recall levels.

Zhang et al.

Texas A&M University

8 / 11

Main Reuslts
Evaluation of Location-Aware TGNs Performance on Scholars@TAMU nad its
Comparative Performance.
MetricValue

AUC-ROC
Average Precision
AUC-ROC (Test)
Average Precision (Test)

0.8615 ± 0.0125
0.8482 ± 0.0129
0.8725 ± 0.0133
0.8712 ± 0.0126

Methods

AUC-ROC Score

Node2Vec + Logistic Regression
Node2Vec + Random Forest
Node2Vec + XGBoost
Node2Vec + LightGBM
CTDNE
GraphSAGE
Location-Aware TGNs

0.5132 ± 0.0210
0.5671 ± 0.0171
0.5812 ± 0.0142
0.6273 ± 0.0194
0.8069 ± 0.0412
0.8117 ± 0.0342
0.8725 ± 0.0133

Zhang et al.

Texas A&M University

9 / 11

Discussion

1Location-Aware TGNs offer several advantages, such as efficient memory
utilization, incorporation of dynamic location information and continuous
temporal information, and the ability to handle spatio-temporal dependencies
within temporal networks.

2This distinction arises from Location-Aware TGNs’ ability to differentiate
between discrete-time dynamic graphs and continuous-time dynamic graphs.

3

One of the key advantages of Location-Aware TGNs is their proficiency inmanaging dynamic location data, which ensures they are not constrained by
the rapid changes in locations.
The advancement of Location-Aware TGNs carries significant potential forrecommendations and can pave the way for broader and more impactful
applications, including practical implementations in fields like transportation
and urban planning, epidemiology, and environmental monitoring.

4

Zhang et al.

Texas A&M University

10 / 11

Thank You!

Zhang et al.

Texas A&M University

11 / 11