## 5 Results
### Effectiveness and Efficiency Evaluation of the Model  
To evaluate the effectiveness of the TWC model on the attribute graph, we compare our Local Extend algorithm with the existing attribute-driven community search (ATC) [15], attributed community query (ACQ) [14], vertex-centric attributed community search (VAC) [18], and the time-weighted overlapping community detection (TWOD) [22]. We randomly selected 100 nodes as query nodes in each experiment and calculated the average value as the experimental result. It is worth noting that TWOD [22] is essentially a global community detection that cannot conduct personalized queries based on user-specified parameters. Therefore, we only compare the TWC and TWOD models from the perspective of user interest drift. In addition, each node may have multiple overlapping communities. For a fair comparison, we take the average value of all overlapping communities of the query node as the result of TWOD in each experiment. * Experiment 1: time distribution of community shared attributes For the community found in the model, we filter the keywords shared with \(q\) and form them into a shared attribute set. We take the historical check-in time of \(q\) as the time-effective window, take the latest check-in timestamp as the start of the window, and divide the time window into sub-windows with a proportion of 10%, 20%, 30%, 40%, and 50%. We calculate the proportion of shared keywords involved in the time window in the entire shared attribute set. As shown in Table 3, the digit in each cell represents the distribution proportion of the attributes in the shared attribute set in the current time window. Because the time window is divided in reverse order by timestamp, the larger the proportion in a smaller time window, the more recent the attribute is, and the closer the short-term interests of community members and the query node are. It is not difficult to find that about 50% of the keywords shared by TWC communities appear in the first 30% of the time window, indicating that community members are closer to the short-term attributes of query node. Although TWOD applies the forgetting curve, the experimental results show that the proportion of shared keywords in the first 30% time window is only 20.7%. The reason is that TWOD ignores the enhancement effect of repeated check-in on user interest and does not consider the possibility that different items may share common keywords. The decay granularity is too rough, resulting in insufficient ability to express users' short-term interests. Other models mainly use high-frequency words as the representative attribute of users, ignoring time, resulting in a low proportion of their shared attributes in the more recent time window, indicating that the current interests of community members and the query node are not well-matched. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\
**Effective** & **Window** & **Window** & **Window** & **Window** & **Window** \\ \hline TWC & 0.115 & 0.237 & 0.478 & 0.686 & 0.814 \\ ACQ & 0.006 & 0.065 & 0.126 & 0.208 & 0.325 \\ ATC & 0.063 & 0.097 & 0.158 & 0.197 & 0.305 \\ VAC & 0.084 & 0.105 & 0.178 & 0.186 & 0.317 \\ TWOD & 0.075 & 0.122 & 0.207 & 0.297 & 0.496 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The distribution proportion of community attributes in different time windows of \(q\). * Experiment 2: community fitness comparison caused by interest drift Because both TWOD and TWC adopt the forgetting curve to model users' interests drift and similarities, they build different communities on this basis. We refer to the definition of fitness in [25] and design the fitness function of the community as follows. \[fitness=\frac{w^{in}}{w^{in}+w^{out}}\] (9) where \(w^{in}\) means the sum of the similarities of all paired nodes in the community, and \(w^{out}\) represents the sum of the similarities between the community nodes and their direct external neighbors.