The course consists of the following components:

• Lectures
  • Lecture 1 (17.1): Slides
  • Lecture 2 (24.1): Slides. Note: The original paper by Barabasi&Albert is unclear on how the preferential attachment models is initialized. The problem seems to be generally solved by connecting the vertices of the initial network to at least one other vertex. Perhaps more elegant would be to use the Laplace correction in the attachment probability definition p(n_i) = (k_i+1)/sum_j(k_j+1), which correspnds to the number of neighbors + the vertex itself. Both approaches will lead to similar network properties enough iterations.
  • Lecture 3 (27.1): Slides.
    • FPF algorithm: Schreiber & Schwöbbermeyer: Frequency concepts and Pattern Detection for the Analysis of Motifs in Networks. Transactions on Computational Systems Biology III. Lecture Notes in Computer Science, 2005, Volume 3737/2005, 89-104
  • Lecture 4 (3.2): Slides.
    • Hartuv, E. Shamir, R.: A clustering algorithm based on graph connectivity. Information Processing Letters Volume 76, Issues 4-6, 31 December 2000, Pages 175-181
  • Lecture 5 (7.2): Slides.
  • Lecture 6 (10.2): Slides.
  • Lecture 7 (14.2): Slides.
  • Lecture 8 (17.2): Slides.
    • J.P Vert: Reconstruction of biological networks by supervised machine learning approaches. Arxiv preprint: http://arxiv.org/abs/0806.0215
• Lecture 9 (24.2): Slides.
• Group work: completed during the group works session, 20% of the grade. Two sessions:
  • Mon 31.1, 10.15am - 11.45am C222, 12.30pm-14.00pm, B222
    • Topic: short presentation (ca. 15 minutes) of a network analysis paper, prepared in the morning session, presented in the afternoon session
    • Group 1: Khadeeja Ismail, Serikzhan Kazi. Zhang & Zhang: A big world inside small world networks
    • Group 2: Juhana Kammonen, Pasi Korhonen. Ravasz & Barabasi: Hierarchical organization in complex networks
    • Group 3: Jia Liu, Marie-Noëlle Specq. Small et al: Scale-free networks which are highly assortative but not small world
    • Group 4: Chengyu Liu, Hongyu Su, Fang Zhou. Callaway et al: Network robustness and fragility: percolation on random graphs
    • Group 5: Meharji Arumilli, Alejandra Cervera Taboada, Shihab Hasan. Stumpf et al: Subnets of scale-free network are not scale-free: Sampling properties of networks
  • Mon 21.2, 10.15am - 11.45am C222, 12.30pm-14.00pm, B222
    • Topic: presentation of software tools and databases for biological network analysis. Each group prepares a presentation of ca. 15-20 minutes of a software/database suitable for analysis of biological network. 
    • Below the people that participated to group work 1 has been preassigned to groups (if you want to participate and you don't see your name in the lists, please contact Juho as soon as possible):
      • Group 1:Khadeeja Ismail, Serikzhan Kazi, Jia Liu: Cytoscape
      • Group 2:Juhana Kammonen, Pasi Korhonen, Marie-Noelle Specq: Biocyc 
      • Group 3: Meharji Arumilli, Alejandra Cervera Taboada, Shihab Hasan:  KEGG  
      • Group 4: Chengyu Liu, Hongyu Su, Fang Zhou: Pajek
    • The above links are pointers to the homepages of the tools. Note that all of them a have a scientific publication or several behind them that explains the internals. Please go and look for them!
    • The groupwork will be graded by looking at (1) information content (2) clarity (3) use of presentation tools. An ideal presentation would answer a question "I have a problem about a biological network at hand. How do I solve it by using this tool?"
• Exercises: completed at home, returned in writing to Leena (leena.salmela@cs.helsinki.fi) prior to the session (don't return late if you want exercise points!), reviewed in the exercise sessions, 30% of the grade. Three sessions
  • Mon 24.1, 10.15-11.45, C222. Exercise set 1. Solutions
  • Mon 7.2, 10.15-11.45, C222 Exercise set 2. Solutions
  • Mon 14.2 10.15-11.45, C222 Exercise set 3. Solutions
  • Extra exercises: Thu 24.2. at 11.15 (second hour of the lecture). Exercise set 4. Solutions
  • Checklist of exercise and groupwork points
• Course exam, 50% of the grade. Examined content are the lectures and the exercises. Group work is not part of the examined contents.
  • Mon 28.2 at 16.00  in A111 

The course will be graded in the scale 1-5. 50% of the maximum points will give the grade of 1/5, 80% of the maximum will give the grade of 5/5.

Schedule