Dynamic Social Network Influence Visualization
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Solution Overview
Problem
Social networks present challenges in analyzing and visualizing vast amounts of dynamic data, with constantly changing interactions and communities, making it difficult to effectively model and predict influence within peer-to-peer networks.
Innovation Solution
The system receives and analyzes data from social networks represented as directed graphs, assigning influence scores to nodes, determining clusters, and dynamically updating visualizations to reflect changes in influence, conversations, and community structures over time, using techniques such as PageRank algorithms and graph partitioning to identify influential nodes and clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If social network data is collected and analyzed to identify influential nodes and clusters, then understanding of social dynamics is improved, but the complexity of data processing and visualization increases
Solution Approach 1:
The patent segments the social network data into distinct clusters of nodes based on interaction patterns and influence scores. By dividing the vast network into manageable clusters, the system can analyze and visualize each cluster separately, reducing the overall complexity while preserving the understanding of social dynamics within and between clusters.
Solution Approach 2:
The patent extracts key features from the social network data, specifically influence scores for each node and interaction patterns between nodes. By extracting only the most relevant features (influence metrics and interaction frequencies) rather than processing all raw data, the system reduces processing complexity while maintaining the essential understanding of social dynamics.
2Measurement precision
If dynamic updates are implemented to reflect changes in influence and community structures, then the accuracy of social analysis is improved, but the computational resources and time required increase
Solution Approach 1:
The patent implements periodic updates of influence scores and cluster configurations at scheduled intervals rather than continuously. This periodic action allows the system to maintain accurate reflections of changing social dynamics while reducing computational overhead by processing updates only at necessary intervals, thus balancing accuracy with time efficiency.
Solution Approach 2:
The patent employs dynamic algorithms that can efficiently detect and respond to changes in the social network structure. The system dynamically adjusts cluster assignments and influence scores based on new interaction data, using optimized algorithms that can quickly identify changes without requiring complete reprocessing of the entire network, thereby maintaining accuracy while reducing computational time.
3Ease of operation
If clusters of nodes are determined and displayed in order of relative importance, then the visualization of influential communities is improved, but the complexity of cluster determination increases
Solution Approach 1:
The patent applies local quality by determining cluster importance based on the specific characteristics and interaction patterns of each individual cluster. Rather than using a single global metric, the system evaluates each cluster's relative importance based on local factors such as internal interaction density, external connections, and aggregate influence scores of its members, enabling nuanced visualization of different community types.
Solution Approach 2:
The patent uses parameter changes in the cluster determination algorithm, specifically adjusting thresholds and weighting factors for different cluster evaluation criteria. By modifying parameters such as the minimum interaction frequency required for cluster formation or the weighting of internal versus external connections, the system can control the complexity of cluster determination while maintaining meaningful visualization of influential communities.
Data Source
AI summary
Data is received characterizing a network represented by a directed graph having nodes and edges. The network includes an influence score associated with a node. The network is associated with a search keyword. A portion of the directed graph and influence score is displayed in a graphical user interface display space. The portion of directed graph is dynamically updated in response to receiving updated network data. Related apparatus, systems, techniques and articles are also described.