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Graph Streaming
Processing paradigm where graph edges arrive sequentially as a stream, requiring algorithms capable of maintaining relevant information with strict memory constraints.
Stream Sampling
Method of probabilistically selecting a representative subset of edges from the stream to estimate properties of the global graph while respecting memory constraints.
Incremental Graph Processing
Approach where computations are progressively updated as new edges arrive, avoiding complete reprocessing of the graph at each modification.
Memory-Efficient Algorithms
Algorithms specifically designed to operate with sublinear memory relative to the graph size, often using compact data structures and approximations.
Real-Time Graph Analytics
Capability to extract relevant information from a graph stream with guaranteed latencies, typically in milliseconds or seconds after the arrival of new edges.
Dynamic Graph Updates
Management of insertions and deletions of edges and nodes in a continuous graph, requiring adaptable data structures and maintenance algorithms.
Approximation Algorithms
Algorithms that provide solutions guaranteed within an approximation factor of the optimal, trading accuracy for memory and time efficiency in the streaming context.
Single-Pass Algorithms
Algorithms that require only one pass through the data stream to produce their result, impossible to re-execute on past data in a streaming environment.
Edge Stream Processing
Sequential processing of graph edges as they arrive in the stream, as opposed to adjacency-based or node-based models.
Temporal Graph Analysis
Study of the evolution of structural properties of a graph over time, capturing dynamics, trends, and emergent patterns in streaming data.
Sketch-Based Methods
Techniques using compact probabilistic data structures to estimate graph properties with theoretical guarantees on relative error.
Graph Summarization
Process of creating a compact representation of a large dynamic graph that preserves essential properties while allowing efficient queries.
Semi-Streaming Model
Computational model where the algorithm has O(n·polylog n) bits of memory for a graph with n nodes, allowing storage of degrees but not all edges.
Turnstile Model
Streaming model where edges can be inserted and deleted, with weights that can be positive or negative, requiring algorithms robust against counterexamples.
W-Stream Model
Model allowing writing of intermediate data to an output stream, relaxing memory constraints at the cost of increased implementation complexity.
Streaming Triangle Counting
Algorithm for estimating the number of triangles in a dynamic graph in real-time, crucial for detecting clusters and social cohesion.