Yves Caseau – Management and Social Networks – February, 2012 1/12
Efficiency of Meetings as a CommunicationEfficiency of ...
Yves Caseau – Management and Social Networks – February, 2012 2/12
MotivationsMotivations
 Affiliation Networks
 Social ...
Yves Caseau – Management and Social Networks – February, 2012 3/12
Coverage SimulationCoverage Simulation
 Communication ...
Yves Caseau – Management and Social Networks – February, 2012 4/12
Metrics : Input (Structure) – Output (Performance)Metri...
Yves Caseau – Management and Social Networks – February, 2012 5/12
Results (meeting size)Results (meeting size)
The larger...
Yves Caseau – Management and Social Networks – February, 2012 6/12
Results (meeting frequency)Results (meeting frequency)
...
Yves Caseau – Management and Social Networks – February, 2012 7/12
Latency: influence of meeting size / distributionsLaten...
Yves Caseau – Management and Social Networks – February, 2012 8/12
Latency: influence of meetings’ frequenciesLatency: inf...
Yves Caseau – Management and Social Networks – February, 2012 9/12
« Small World » Structures : Hybrid Networks« Small Wor...
Yves Caseau – Management and Social Networks – February, 2012 10/12
Approximate Formula for LatencyApproximate Formula for...
Yves Caseau – Management and Social Networks – February, 2012 11/12
Optimizing the use of communication channelsOptimizing...
Yves Caseau – Management and Social Networks – February, 2012 12/12
ConclusionsConclusions
 Analysis of Affiliation Netwo...
Prochain SlideShare
Chargement dans…5
×

Management socialnetworksfeb2012

577 vues

Publié le

Talk given during the "Management and Social Networks" conference in Geneva (2012). Towards a "theory of meeting", with a focus on meeting systems, efficiency, affiliation network, information propagation.

Publié dans : Sciences
  • Soyez le premier à commenter

  • Soyez le premier à aimer ceci

Management socialnetworksfeb2012

  1. 1. Yves Caseau – Management and Social Networks – February, 2012 1/12 Efficiency of Meetings as a CommunicationEfficiency of Meetings as a Communication Channel: A Social Network AnalysisChannel: A Social Network Analysis MSN 2012, Geneva February 16th , 2012 Yves Caseau – Bouygues Telecom National Academy of Technologies of France
  2. 2. Yves Caseau – Management and Social Networks – February, 2012 2/12 MotivationsMotivations  Affiliation Networks  Social Network for which links are N-to-M versus 1-to-1, either represented as an hyper-graph or a bipartite (two- mode) graph  CMS (Corporate Meeting System)  « System » of scheduled meetings in a company  A favorite topic of interest for management consultants  TDC example: the strength of short daily meetings  Meetings make a key communication channel in large enterprises, because of the amount of time that is spent  Describing Affiliation Networks  Diameter (set of persons met in a month)  Degree (number of meetings that one attends in a month)  Cluster Rate: transitivity ratio (keeping within small groups)  A number of tools/metrics are available: [LMD08] M. Latapy, C. Magnien and N. Del Vecchio, “ Basic Notions for the Analysis of Large Two-mode Networks ”, Social Networks, Vol. 30, n° 1, Jan 2008. Di (informationnel) Dr (Diam. Réunionnel)
  3. 3. Yves Caseau – Management and Social Networks – February, 2012 3/12 Coverage SimulationCoverage Simulation  Communication needs may be represented with a social network  Valued with contact frequencies  Typical size: 200 to 2000 nodes  Typical structure (degree, cluster, …)  « Coverage » means to build an affiliation networks which covers contact requirements, either through directs edges or through short paths  This is consistent with the way actual meetings are designed (need to capture regular interactions of a set of people on a given topic)  Random TVSN generation (time-valued social networks)  Various cluster rate (from random graph to heavy clusters)  Various degree distribution (from regular to power laws)  Various contact frequency distribution (regular to exponential)  Coverage heuristic: greedy algorithm that produces a set of hyper-edges which contains the most significant edges from the input TSVN Carol Lucy 1h / week 1h / week 1h / w 1h / week 2h / month 2h / m 1h / m 1h / m 2h /w 1h / m 1h / month 1h / 2 days 1h / 2d 1h / 2 days 1h / 2d 1h / w Peter Mary Luke Jane Bob
  4. 4. Yves Caseau – Management and Social Networks – February, 2012 4/12 Metrics : Input (Structure) – Output (Performance)Metrics : Input (Structure) – Output (Performance) Communication requirements  Captured by TVSN  Degree of TSVN → Di  Contact Frequency Distribution CMS structure • Average size (A) • Number of meetings (M) • Average Frequencies (Fm) Four metrics for communication performance: • Latency is the speed of information propagation. It is measured though the average distance between two nodes • Throughput is the ability from the meeting system to transport information. It is measured as the sum of the products (duration x frequency) for all meetings. • Feedback is defined as the ability to check appropriation/understanding when some information is transmitted. • Loss is the opposite to the capacity to transport information without change. The simplest measure is the average path length. N: Number of people A R : number of meetings/person T = 100 (100h of meetings/committee per month) F: frequency of each meeting 1/100 3/100 3/100 3/100 M : number of meetings Modulo a few constraints (« simple laws »)  Fm * R = T  M * Fm = N / A * T Consequently, two trade-offs must be found:  For each person, between few frequent meetings and many infrequent meetings  Generally, few large meetings or many small meetings. Topic of study One of many dimensions ! Notourtopichere
  5. 5. Yves Caseau – Management and Social Networks – February, 2012 5/12 Results (meeting size)Results (meeting size) The larger the meeting attendance, the better the latency  At the expense of throughput (and feedback)  Improvement of loss, larger meeting diameter
  6. 6. Yves Caseau – Management and Social Networks – February, 2012 6/12 Results (meeting frequency)Results (meeting frequency) Frequent meetings provide latency improvement  The loss in Dm is more than compensated by the improvement with the individual meeting latency  No degradation of bandwidth (small improvement)  Small degradation of loss
  7. 7. Yves Caseau – Management and Social Networks – February, 2012 7/12 Latency: influence of meeting size / distributionsLatency: influence of meeting size / distributions  Latency decreases with meeting sizes, as well as path length, but so does « feedback ». Special case More efficient (known result ) Other form of « power law »
  8. 8. Yves Caseau – Management and Social Networks – February, 2012 8/12 Latency: influence of meetings’ frequenciesLatency: influence of meetings’ frequencies  Frequent meetings produce better latency, better throughput at the expense of longer paths.
  9. 9. Yves Caseau – Management and Social Networks – February, 2012 9/12 « Small World » Structures : Hybrid Networks« Small World » Structures : Hybrid Networks High Frequency Meetings  Hybridization (mixing meetings obtained with different control parameters) produces « small world structures » in the sense of Duncan Watts  “… networks which displayed the high local clustering of disconnected caves but were connected such that any node could be reached from any other in an average of a few steps”.  Hybrid Affiliation Networks increases communication performance (both latency and throughput)
  10. 10. Yves Caseau – Management and Social Networks – February, 2012 10/12 Approximate Formula for LatencyApproximate Formula for Latency D = [log(Di) / log(Dr)] * R  Actually an exact formula for simple cases  Following table example : standard deviation less than 10%, average is close to 100% (of actual value) 0 20 40 60 80 100 120 140 160 180 200 0 100 200 300 400 500 DR ratio D*10
  11. 11. Yves Caseau – Management and Social Networks – February, 2012 11/12 Optimizing the use of communication channelsOptimizing the use of communication channels  Application of BPEM (Business Process Enterprise Model) to study the impact of communication channels on performance  Four categories of communication channels  “Communication Channel Model”  Characteristics  Policies Communication Channel Model BPEM Results (value) Learning (optimization) Activities to be assigned to resources Channel PoliciesCommunication flow units to be scheduled Scheduler Receivers Organization Rules/ Culture Information Flows Meetings Face-to-Face Electronic – Synchronous Electronic – Asynchronous • Randomization (Monte-Carlo) • Evolutionary algorithms (learning): local opt, genetic algorithm Channel Performance Characteristics: Throughput, Latency, Loss, Scheduling constraints Cf. Previous Formula 
  12. 12. Yves Caseau – Management and Social Networks – February, 2012 12/12 ConclusionsConclusions  Analysis of Affiliation Networks which represent « corporate meeting systems » is relevant to characterize and optimize information flows  i.e., although communication is but one of meetings’ goals, and structural efficiency only one dimension of communication efficiency, this is a critical dimension for large companies.  Computer simulation confirms lessons from experience:  Frequent meetings (hence less numerous) should be favored  There should be a mix of small attendance meetings with larger ones  More generally, communication flows optimization is a key component of organization and management theory for 21st century enterprises  Characterization of communication channels  Understanding information flows that are generated through business processes  Towards a « theory of meetings »: structure, semantics and dynamics Yves CASEAU

×