Co-evolution of network structure and content

1. Co-evolution of network structure and content Lada Adamic School of Information & Center for the Study of Complex Systems University of Michigan

2. Outline • Co-evolution of network structure and content • Can the structure of Twitter and virtual world interactions reveal something about their content? • http://arxiv.org/abs/1107.5543 • Can the structure of a commodity futures trading network reveal something about information flowing into the market? • http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1361184

3. What is the relationship between network structure and information diffusion?

4. Is information flowing over the network?Or is information shaping the network?

5. Can the shape of the network reveal properties of information Big news! Giant microbes!

6. Can the shape of the network reveal properties of information Little news. How’s the weather?

7. Related work on time evolving graphs • Densification over time (Leskovec et al. 2005) • Community structure over time (Leicht et al. 2007, Mucha et al. 2010) • Change in structure (ability to “compress” network) signals events (Graphscopeby Sun et al. 2007) • Disease propagation & timing (Moody 2002, Liljeros 2010) • Enron email (B. Aven, 2011)

8. What’s different here We look at network dynamics at relatively short time scales and construct time series A range of network metrics, instead of just community structure Information novelty and diversity as opposed to tracking single events / pieces of information

9. Can the network reveal… If everyone is talking about the same thing, or if there is just background chatter. If what they are talking about is novel?

10. 1st context: virtual worlds Networks: asset transfers (gestures, landmarks) and transactions (e.g. rent, object purchases) Content: assets being transferred

11. Study transfers in the context of 100 groups with highest numbers of transfers

12. Second context: Twitter Network • Retweet: repeat something someone else wrote on twitter, preceded by the letters RT and @ in front of their username • microblogging : < 140 characters / tweet • Network links read from tweets • Reply or mention: by putting the @ in front of the username

13. Selecting Twitter communities to track http://wefollow.com/twitter/researcher For each “researcher” gather tweets of accounts they follow

14. Highly dynamic networks • Segmentation: • Twitter: every 800 tweets • median segment duration 1.5 days • SecondLife: every 50 asset transfers • median segment duration 8.4 days % of edges repeated Segments elapsed

15. Conductance:capturing potential for information flow A B A B A B low conductance medium conductance high conductance • Temporal conductance (summed over all pairs): • High if pairs of nodes share edges, or many short, indirect paths Koren, North, Volinsky, KDD, 2006

16. Network expectedness expected unexpected • Define expectedness: • Average conductance of all neighbor pairs at time t, • based on conductance of pair at time t-1

17. Conductance and expectedness as a toy network evolves a network configuration at t = 0 b c conductance = 4 a a a possible configurations at t = 1 b b b d c c c conductance = 4 expectedness = 1.5 edge jaccard = 1 conductance = 4.5 expectedness = 1.3333 edge jaccard = 0.6667 conductance = 6 expectedness = 0.5 edge jaccard = 0.25

18. SecondLife: network structure and content standard network metrics are not indicative of information properties conductance and expectedness are overlap t-1,t overlapt,t+1 D diversityt-1, t D diversityt, (t+1)

19. Conductance & diversity of information Social and transaction network of top sellers in SL High conductance brings higher content diversity Repeat network patterns bring less diversity and less novelty but… similarity and novelty are positively correlated (r = 0.19)

20. Twitter: textual diversity and novelty • Semantic metrics

21. Twitter: network structure and information diversity network structure content similarity

22. Inferring Network Semantic Information Semantic variables Kernel Regression Prediction Model Semantic variables Topological variables Question: Does the network structural information help to improve the prediction performance of the characteristics of information exchanged?

23. Example: Inferring the average similarity score between isolated pairs The input variables of curve ci start from Xiand increase each time by adding the variable labeled on x-axis. • Don’t need to use other textual variables (e.g. similarity between indirectly connected pairs) when sufficient topological information available • Reason: topological variables account for much of the pattern in the text!

24. Network structure and information novelty • Greater novelty in edges corresponds to greater novelty in content shared • For nodes that are interacting (citing or being cited): • Higher conductance and expectedness correlates with less information novelty

25. Information in trading networks • Collaboration with CelsoBrunetti, Jeff Harris, and Andrei Kirilenko • http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1361184 CFTC = Commodity futures trading commission stated mission: protect market users and the public from fraud, manipulation, and abusive practices futures contracts started out as contracts for agricultural products, but expanded to more exotic contracts, including index futures

26. Data executing broker opposite broker quantity: 10 price: $171.25 6.3 million transactions in Aug. 2008 in the Sept. E-mini S&P futures contract price discovery for the index occurs mostly in this contract (Hasbrouck (2003)) data includes: date & time, executing broker, opposite broker, buy or sell, price, quantity sample in transaction windows of 240 transactions

27. matching algorithm sell 10 contractsat $171.25 buy 30 contractsat $171.25 sell 5 contracts at $171.75 buy 20 contractsat $171.50 sell 20 contracts at $172.00 buy 50 contractsat $171.00 buy 20 contractsat $171.50 buy 30 contractsat $171.25 limit order book

28. not social, not intentional, not persistent

29. Financial variables Rate of return: Last price to first price in logs (close-to-open) Volatility: Range – log difference between max and min price Duration: Total period duration - time in seconds between the start and end of each sampling period Proxy for arrival of new information Volume: Trading volume – number of contracts traded

30. What can we learn from network structure?e.g. centralization? low outdegree low indegree high indegree high outdegree

31. overview of network variables # nodes, # edges clustering coefficient, LSCC, reciprocity CEN = giniin-degree – giniout-degree INOUT = r(indegree of node, outdegree of same node) AI (asymmetric information)

32. Correlations between network and financial variables High Centralization: market dominance - a dominant trader buys from many small sellers – low duration, low volume

33. Correlations between network and financial variables Negative assortativity: large sellers sell to small buyers and vice versa– low duration, higher volume

34. Correlations between network and financial variables High av. degree & largest strongly connected component: no news - many buyers and sellers – high duration, high volume

35. Correlations between network and financial variables Rate of return: positive correlation with centralization Volatility & duration:correlated with standard deviation of degree, average deg. and the total number of edges (E). Volume:Correlated with a few network variables, sign varies.

36. Conclusion Network structure alone is revealing of the diversity and novelty information content being transmitted Results depend on the scope and relative position of the activity in the network

37. Future work INARC Sensitivity to inclusion of non-interactive or across-community interactions Applying novelty & conductance metrics to financial time series Continuous formulation of novelty and other network metrics (because segmentation is problematic) Roles of individual nodes Thanks: Edwin TengLiuling Gong AvishayLivne Information network academic research center

38. Questions?