Spatial-Temporal Social Network Prediction for Malicious Activity
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Solution Overview
Problem
Current systems lack effective methods for predicting malicious social activities using high-dimensional spatial-temporal social network data, which is crucial for monitoring and preventing activities such as terrorism, drug dealing, and criminal behavior.
Innovation Solution
A system that extracts spatial structures from short-term social network data and temporal structures from long-term data, representing social activities as activity cores with computed statistics to predict future behaviors, including geolocation and timing, using graph matching techniques and participation profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-dimensional spatial-temporal social network data is used to predict malicious activities, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex prediction task into distinct spatial and temporal components. Spatial structures are extracted from short-term data (hours to days) while temporal structures are extracted from long-term data (weeks to months). This segmentation allows the system to handle high-dimensional data by processing it in manageable, structured components rather than as a monolithic complex problem.
Solution Approach 2:
The patent transforms the high-dimensional social network data into a graph network representation with nodes and edges, adding a structural dimension to the analysis. By representing social activities as clusters of nodes and edges, and defining activity cores with participation profiles, the system converts complex high-dimensional data into a multi-dimensional graph structure that is more tractable for prediction while maintaining accuracy.
2Reliability
If spatial and temporal structures are extracted from social network data, then malicious activity prediction capability is improved, but data processing time increases
Solution Approach 1:
The patent performs preliminary extraction of spatial structures from short-term data and temporal structures from long-term data before the actual prediction process. By pre-processing the data to identify and structure spatial clusters and temporal patterns in advance, the system reduces the computational burden during real-time prediction, thereby improving prediction capability while minimizing additional processing time.
Solution Approach 2:
The patent employs dynamic graph matching techniques that adapt to changing social network structures over time. The graph matching algorithm dynamically adjusts to the evolving spatial and temporal structures, allowing efficient processing of large datasets by focusing computational resources on relevant changing patterns rather than reprocessing entire datasets, thus improving prediction reliability without excessive time loss.
Data Source
AI summary
Described is a system for predicting future social activity. The system extracts social activities from spatial-temporal social network data collected in a first time period ranging from hours to days to capture spatial structures of social activities in a graph network representation. A graph matching technique is applied over a set of spatial-temporal social network data collected in a second time period ranging from weeks to months to capture temporal structures of the social activities. A spatial-temporal structure of each social activity is represented as an activity core, where each activity core is defined as active nodes that participate in the social activity with a frequency over a predetermined threshold over the second time period. For each activity core, the system computes statistics of the social activity and uses the statistics to generate a prediction of future behaviors of the social activity.