Exploration of temporal graphs with bounded degree Thomas Erlebach - PowerPoint PPT Presentation

Exploration of temporal graphs with bounded degree Thomas Erlebach and Jakob Spooner University of Leicester { te17 | jts21 } @leicester.ac.uk ICALP Workshop “Algorithmic Aspects of Temporal Graphs” 9 July 2018 Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018

Outline 1 Temporal graphs 2 Temporal graph exploration problem (TEXP) 3 Known results Instances that require Ω( n 2 ) steps 4 Faster exploration of degree-bounded graphs 5 Conclusions Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 1 / 20

Temporal Graphs Temporal graph (Dynamic, time-varying graph) A graph in which the edge set can change in every (time) step. Step 0: Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 2.1 / 20

Temporal Graphs Temporal graph (Dynamic, time-varying graph) A graph in which the edge set can change in every (time) step. Underlying graph The graph with all edges that are present in at least one step. Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 2.4 / 20

Temporal (Time-Respecting) Path Time edge A pair ( e , t ) where e is an edge of the underlying graph and t is a time step when e is present. Temporal path (journey) A sequence of time edges ( e 1 , t 1 ) , . . . , ( e k , t k ) such that ( e 1 , e 2 , . . . , e k ) is a path in the underlying graph and t 1 < t 2 < · · · < t k . Example: 3 9 7 4 Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 3.1 / 20

Temporal (Time-Respecting) Path Time edge A pair ( e , t ) where e is an edge of the underlying graph and t is a time step when e is present. Temporal path (journey) A sequence of time edges ( e 1 , t 1 ) , . . . , ( e k , t k ) such that ( e 1 , e 2 , . . . , e k ) is a path in the underlying graph and t 1 < t 2 < · · · < t k . Example: 3 9 7 4 Temporal walk : temporal path where vertices may repeat Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 3.2 / 20

Temporal Graph Exploration Temporal graph exploration problem (TEXP) Starting at a given vertex s at time 0, find a fastest temporal walk that visits all vertices. Equivalently: Schedule an agent: In each time step, traverse an edge or wait. Minimize time when last vertex is visited. Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 4.1 / 20

Temporal Graph Exploration Temporal graph exploration problem (TEXP) Starting at a given vertex s at time 0, find a fastest temporal walk that visits all vertices. Equivalently: Schedule an agent: In each time step, traverse an edge or wait. Minimize time when last vertex is visited. We assume: The whole temporal graph is known in advance. Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 4.2 / 20

Temporal Graph Exploration Temporal graph exploration problem (TEXP) Starting at a given vertex s at time 0, find a fastest temporal walk that visits all vertices. Equivalently: Schedule an agent: In each time step, traverse an edge or wait. Minimize time when last vertex is visited. We assume: The whole temporal graph is known in advance. Michail and Spirakis [MFCS’14] It is NP-complete to decide if a temporal graph can be explored if it need not be connected in each step. Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 4.3 / 20

Temporal Graph Exploration Temporal graph exploration problem (TEXP) Starting at a given vertex s at time 0, find a fastest temporal walk that visits all vertices. Equivalently: Schedule an agent: In each time step, traverse an edge or wait. Minimize time when last vertex is visited. We assume: The whole temporal graph is known in advance. Michail and Spirakis [MFCS’14] It is NP-complete to decide if a temporal graph can be explored if it need not be connected in each step. ⇒ Like Michail and Spirakis, we consider temporal graphs that are connected in each step and have lifetime ≥ n 2 . (Note: We consider undirected graphs only.) Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 4.4 / 20

Temporal Graph Exploration Temporal graph exploration problem (TEXP) Starting at a given vertex s at time 0, find a fastest temporal walk that visits all vertices. Equivalently: Schedule an agent: In each time step, traverse an edge or wait. Minimize time when last vertex is visited. We assume: The whole temporal graph is known in advance. Reachability lemma: Let G be a temporal graph with n vertices. Agent can reach any vertex v from vertex u in n time steps. Proof. Since G always has a u - v path, the set of vertices reachable from u increases in each step until v is reached. Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 4.5 / 20

Temporal Graph Exploration Temporal graph exploration problem (TEXP) Starting at a given vertex s at time 0, find a fastest temporal walk that visits all vertices. Equivalently: Schedule an agent: In each time step, traverse an edge or wait. Minimize time when last vertex is visited. We assume: The whole temporal graph is known in advance. Reachability lemma: Let G be a temporal graph with n vertices. Agent can reach any vertex v from vertex u in n time steps. Corollary Any temporal graph can be explored in n 2 time steps. Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 4.6 / 20

Example Instance of Temporal Graph Exploration problem: Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 5.1 / 20

Example Temporal exploration completed in Step 5. Step 0 Step 1 Step 2 Step 3 Step 4 Step 5 Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 5.6 / 20

Previous Work on TEXP Avin, Kouck´ y, Lotker, ICALP’08: Analyze cover time of random walk in temporal graph (with self-loops) Star construction shows that simple random walk may take Ω(2 n ) steps Lazy random walk that leaves v only with probability deg( v ) / (∆ + 1) has cover time O (∆ 2 n 3 log 2 n ) Michail and Spirakis, MFCS’14: D -approximation algorithm for temporal graph exploration, where D is the dynamic diameter Note: 1 ≤ D ≤ n − 1, can be equal to n − 1 No (2 − ε )-approximation algorithm unless P = NP (1 . 7 + ε )-approximation algorithm for temporal TSP with dynamic edge weights in { 1 , 2 } Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 6 / 20

Previous Work on TEXP E, Hoffmann, Kammer, ICALP’15: Instances of TEXP that require Ω( n 2 ) steps No O ( n 1 − ε )-approximation algorithm unless P = NP Results for restricted underlying graphs: treewidth k : O ( n 1 . 5 k 1 . 5 log n ) steps planar: O ( n 1 . 8 log n ) steps cycle, cycle with chord: O ( n ) steps 2 × n grid: O ( n log 3 n ) steps Instances of TEXP where underlying graph is planar with ∆ = 4 that require Ω( n log n ) steps Further results on temporal graphs with randomly present edges or regularly present edges. Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 7 / 20

TEXP instances that require Ω( n 2 ) steps Consider the temporal graph below that is a star in each step. Let c 0 be the center of a star in step 0. c 0 v 0 c 1 v 1 c 2 v 2 c 3 v 3 c 4 v 4 c 5 v 5 Thomas Erlebach and Jakob Spooner ICALP Workshop, Prague, 9 July 2018 8.1 / 20

Exploration of temporal graphs with bounded degree Thomas Erlebach - PowerPoint PPT Presentation

Exploration of temporal graphs with bounded degree Thomas Erlebach and Jakob Spooner University of Leicester { te17 | jts21 } @leicester.ac.uk ICALP Workshop Algorithmic Aspects of Temporal Graphs 9 July 2018 Thomas Erlebach and Jakob

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