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The Layered World of Scientific Conferences Michael Kuhn Roger Wattenhofer Distributed Computing Group APWEB 2008 Shenyang, China The Proximity of Scientific Conferences • – How does the proximity of conferences look like? • Different aspects of proximity – Scope – Quality • APWEB The web around APWeb Why do we care about conference proximity? Michael Kuhn, ETH Zurich @ APWEB 2008 1. WAIM 2. WISE 3. GCC 4. DASFAA 5. SKG 6. ISPA 7. PDCAT 8. DEXA 9. ICDF 10. PAKDD 2 Application: Conference Search • Different search types – – – • Based on DBLP – • For related conferences By keywords By author Freely available Wiki-Approach for some attributes – – – Important dates Location Link to website Try it at www.confsearch.org! Michael Kuhn, ETH Zurich @ APWEB 2008 3 4 5 6 7 8 „Social similarity“ and the Conference Graph • A single author tends to submit to similar conferences – Conferences C1 and C2 are similar if many authors often submit to both of them – Data available from DBLP • Problem: Conferences have unequal „size“ – Just counting the number of authors over-estimates the proximity of large venues – Normalization required: A1 p11/s1 = 3/25 1/10 1/10 pi1 pi 2 T min , i s1 s2 1/25 A2 1/25 2/10 C2 C1 5/25 A3 5/25 4/10 T = 17/50 Michael Kuhn, ETH Zurich @ APWEB 2008 9 Michael Kuhn, ETH Zurich @ APWEB 2008 10 Some Examples Symposium on Parallel Algorithms & Architectures Structural Information & Communication Complexity Int. Conference on Distributed Computing Systems PODC AAAI Principles of Distributed Computing National Conference on Artificial Intelligence DISC 1.00 IJCAI 0.76 OPODIS 0.49 ATAL 0.37 SPAA 0.46 ICML 0.33 SIROCCO 0.36 AGENTS 0.32 ICDCS 0.32 AIPS 0.31 SRDS 0.30 ECAI 0.26 STOC 0.27 KR 0.25 SODA 0.24 UAI 0.25 FOCS 0.22 CP 0.23 DIAL-M 0.21 FLAIRS 0.20 Agent Theories, Architectures, and Languages European Conference on Artificial Intelligence Proximity is not purely thematic! Michael Kuhn, ETH Zurich @ APWEB 2008 11 The Concept of Layers • Layers correspond to different reasons (catalysts) for edges – Thematic scope and quality are such reasons – Similar to the concept of „social dimensions“ of Watts, Dodds, Newman (2002) • Total graph is the sum of its layers: (i ) Tuv xi wuv i Michael Kuhn, ETH Zurich @ APWEB 2008 12 Thematic Layer • Comparing publication titles allows to estimate thematic similarity of conferences – Score for each conference-keyword pair • TF-IDF (Term-Frequency Inverse-Document-Frequency) – Similarity: cardinality of the intersection of the top-50 keywords PODC 1. Byzantine 2. Consensus 3. Quorum 4. Wait 5. Exclusion 6. Detectors 7. Distributed 8. Networks 9. Asynchronous 10. Stabilizing ... ICDCS 1. Distributed 2. Networks 3. Wireless 4. Exclusion 5. Multicast 6. Consistency 7. Mobile 8. Hoc 9. Protocol 10. ad ... SPAA 1. Parallel 2. Scheduling 3. Routing 4. Oblivious 5. Adversarial 6. Networks 7. Memory 8. Load 9. Stealing 10. Algorithms ... Michael Kuhn, ETH Zurich @ APWEB 2008 AAAI 1. Learning 2. Planning 3. Robot 4. Reasoning 5. Knowledge 6. Search 7. Agent 8. Constraint 9. AI 10. Reinforcement ... 13 Layer Separation by Subtraction • Assumption: 2 major layers: thematic layer (t) and quality layer (q) – Total weight T = x1t + x2q + x3r – Remainder r is neglected q ≈ T - αt Quality layer Social similarity (total weight) Thematic layer • The qualitative similarity q can be determined from T and t! – Result is only a rough estimate due to considerable simplifications (independence of layers, neglecting r, etc.) Michael Kuhn, ETH Zurich @ APWEB 2008 14 Example: Thematic and Quality Layer for AAAI Michael Kuhn, ETH Zurich @ APWEB 2008 15 Proximity Based Conference Rating (1) • In the quality layer a tier-1 conference is supposed to have many tier-1 conferences in its proximity (the same holds for tier-2 and tier-3) – Unknown ratings can be „interpolated“ – Intial ratings taken from Libra (MSR Asia) – Existing approaches mostly citation based (initiated by Garfield in 1972) ? Michael Kuhn, ETH Zurich @ APWEB 2008 Median 16 Proximity Based Conference Rating (2) Intial ratings taken from Libra – Libra vs. „Internet List“: „Error“-rate 34.5% – Conference rating is difficult and partly subjective – Tier-1 vs. Tier-3: 4.5% Error (α = 0) 0.7 1) Roughly detect tier (1,2 vs. 2,3) 2) Use specific Alpha for fine separation 0.6 Error (fraction) • Tier-3 Total 0.5 Tier-2 0.4 Tier-1 0.3 0 0.2 0.4 0.6 0.8 Alpha Michael Kuhn, ETH Zurich @ APWEB 2008 1 Recall: q ≈ T - αt 17 Proximity Based Conference Rating (3) Libra vs. „Internet List“: 34.5% Random: 66.7% Total error drops from 50.5% to 40.3% After „thematic correction“: 40.3% Diagonal elements dominate Estimated Tier Tier (Libra) Total graph: 50.5% T1 T2 T3 % Correct T1 54 28 3 64% T2 38 112 48 57% T3 19 92 172 61% Few „serious“ errors: 22 of 567 = 3.9% Michael Kuhn, ETH Zurich @ APWEB 2008 18 Conclusion and Future Work • We have seen that – – – – „Social similarity“ is a good measure to relate conferences „Social similarity“ consists of thematic and a quality layer The thematic layer can be estimated using publication titles The quality layer can be emphasized by subtracting the thematic component – These ideas can be used for conference rating and search • www.confsearch.org • It would be interesting to look at – A generic method for layer separation (that works on various graphs) – Looking at combinations of the presented conference rating ideas with citation based approaches Michael Kuhn, ETH Zurich @ APWEB 2008 19 Thanks for Your Attention • Questions? Michael Kuhn, ETH Zurich @ APWEB 2008 20